diff --git a/.gitignore b/.gitignore index 14eb9824b..0b2fa55ae 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,10 @@ #### Temporary files -experiments/annual_reviews/figure6/trained_gp_model/bak_best_model_*.pth -experiments/annual_reviews/figure7/safe_exp_results/ -experiments/annual_reviews/figure8/unsafe_ppo_model/bak_unsafe_ppo_model_30000.pt -experiments/annual_reviews/figure8/unsafe_ppo_temp_data/ +examples/lqr/*data/ # -experiments/arxiv/cartpole_*/*data/ -experiments/arxiv/quadrotor_*/*data/ +examples/mpsc/temp-data/ +examples/mpsc/unsafe_rl_temp_data/ +# +examples/pid/*data/ # results/ z_docstring.py @@ -13,7 +12,6 @@ TODOs.md - # macOS users .DS_Store @@ -151,11 +149,10 @@ dmypy.json .pyre/ .idea/ aer-course-project/lab3/image_folder/processed_frames/ -aer-course-project/lab3/image_folder/input_frames/ - +aer-course-project/lab3/image_folder/ aer-course-project/edit_this_original.py - aer-course-project/PlanningAlgorithm/ -# Local settings -.idea/ +#lab4 CityData dir +CityData/ +video.avi diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..8394b8177 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,58 @@ +# Install the pre-commit hooks below with +# 'pre-commit install' + +# Auto-update the version of the hooks with +# 'pre-commit autoupdate' + +# Run the hooks on all files with +# 'pre-commit run --all' + +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: check-ast + - id: check-yaml + - id: check-toml + - id: check-added-large-files + args: ['--maxkb=10000'] + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: check-shebang-scripts-are-executable + - id: check-merge-conflict + - id: debug-statements + - id: trailing-whitespace + - id: end-of-file-fixer + - id: double-quote-string-fixer + +- repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + name: isort + args: ['--line-length=110'] + +- repo: https://github.com/pre-commit/mirrors-autopep8 + rev: v2.0.4 + hooks: + - id: autopep8 + name: autopep8-default + args: ['-i', '--ignore=C0301', '--max-line-length=1000'] + exclude: (^tests/|^safe_control_gym/math_and_models/transformations.py) + - id: autopep8 + name: autopep8-tests + args: ['-i', '--ignore=C0301,E501,E201,E241,E127', '--max-line-length=1000'] + files: (^tests/|^safe_control_gym/math_and_models/transformations.py) + +- repo: https://github.com/PyCQA/flake8 + rev: 6.1.0 + hooks: + - id: flake8 + name: flake8_default + args: ['--ignore=E501'] + exclude: (^safe_control_gym/__init__.py|^tests/|^safe_control_gym/math_and_models/transformations.py) + - id: flake8 + name: flake8_tests + args: ['--ignore=E501,E201,E241,E127'] + files: (^tests/|^safe_control_gym/math_and_models/transformations.py) + exclude: ^tests/test_build.py diff --git a/aer-course-project/Lab4/downloadCityData.py b/aer-course-project/Lab4/downloadCityData.py new file mode 100644 index 000000000..f490e8d4c --- /dev/null +++ b/aer-course-project/Lab4/downloadCityData.py @@ -0,0 +1,25 @@ +import os +import zipfile +import gdown + +_TARGET_DIR = os.path.expanduser('..') #Directory to store data +_FILE_ID= "1368NySV1bWwlOhp525TD0HJEvbCG0WZr" #Use Id for zip file on drive, ensure "Anyone with link can access" + + +def save_with_gdown(id, destination): + url = 'https://drive.google.com/uc?id='+id + gdown.download(url, destination, quiet=False) + + + +if __name__ == '__main__': + zip_path = _TARGET_DIR + '/CityData.zip' + os.remove(zip_path) if os.path.exists(zip_path) else None + save_with_gdown(_FILE_ID, zip_path) + try: + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(_TARGET_DIR) + os.remove(zip_path) + + except zipfile.BadZipFile: + print('Not a zip file or a corrupted zip file') diff --git a/aer-course-project/Lab4/downloader.sh b/aer-course-project/Lab4/downloader.sh new file mode 100755 index 000000000..db35e0cae --- /dev/null +++ b/aer-course-project/Lab4/downloader.sh @@ -0,0 +1,5 @@ +pip install gdown +pip install opencv-contrib-python +python3 downloadCityData.py +clear +echo "Finished extracting CityData folder" \ No newline at end of file diff --git a/aer-course-project/getting_started.yaml b/aer-course-project/Lab4/getting_started.yaml similarity index 100% rename from aer-course-project/getting_started.yaml rename to aer-course-project/Lab4/getting_started.yaml diff --git a/aer-course-project/Lab4/ground_truth_pose.mat b/aer-course-project/Lab4/ground_truth_pose.mat new file mode 100644 index 000000000..1c37a8766 Binary files /dev/null and b/aer-course-project/Lab4/ground_truth_pose.mat differ diff --git a/aer-course-project/Lab4/lab4.py b/aer-course-project/Lab4/lab4.py new file mode 100644 index 000000000..df264da95 --- /dev/null +++ b/aer-course-project/Lab4/lab4.py @@ -0,0 +1,153 @@ +""" +2021-02 -- Wenda Zhao, Miller Tang + +This is the code base for a steoro visual odometry designed +for the course AER 1217H, Development of Autonomous UAS +https://carre.utoronto.ca/aer1217. +The Kitti dataset, raw_data/City/2011_09_26_drive_0005, is used in this assignment. +http://www.cvlibs.net/datasets/kitti/raw_data.php +""" + +# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +# Kitti Data cannot be saved on Github, too large (340 MB) +# run the following command to download data locally +# `./downloader.sh` +# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + +# ANDRES RANSAC BRANCH +# import pkg_resources +# pkg_resources.require("cv2==8.2.0") + +import numpy as np +import cv2 as cv +import os +import scipy.io as sio +# from mpl_toolkits.mplot3d import Axes3D +import matplotlib.pyplot as plt + +from stereo_vo_base import StereoCamera, VisualOdometry + + +# pip install opencv-contrib-python + +def main(): + # current working directory + cwd = os.getcwd() + + # the ground_truth_pose.mat is saved from the kitti 'raw_data_development_kit'->run_demoVehicelPath.m + # see http://www.cvlibs.net/datasets/kitti/raw_data.php for more details + OXTS_pose_gt = sio.loadmat('ground_truth_pose.mat')["pose"] + + # image sequence + sequence_num = OXTS_pose_gt.shape[1] + pose_gt = np.zeros((sequence_num, 4, 4)) + # save ground truth into pose_gt + for i in range(sequence_num): + pose_gt[i] = OXTS_pose_gt[0, i] + + # ---------------------------- Parameter ---------------------------- # + path_0 = '/CityData/2011_09_26/2011_09_26_drive_0005_sync/image_00/data/' + path_1 = '/CityData/2011_09_26/2011_09_26_drive_0005_sync/image_01/data/' + zero_num = 10 + # '/CityData/2011_09_26_calib/calib_cam_to_cam.txt' + # baseline, focalLength, fx, fy, cu, cv + cam = StereoCamera(0.537, 721.5377, 721.5377, 721.5377, 609.5593, 172.8540) + vo = VisualOdometry(cam) + + # global transform to camera (starts as identity) + T = np.eye(4) + T_hist = np.zeros((sequence_num, 4, 4)) + T_hist[0] = T + + # convert to vehicel frame + # calibration results provided by kitti dataset in '/CityData/2011_09_26_calib/' + T_imu_to_velo = np.array([[9.999976e-01, 7.553071e-04, -2.035826e-03, -8.086759e-01], + [-7.854027e-04, 9.998898e-01, -1.482298e-02, 3.195559e-01], + [2.024406e-03, 1.482454e-02, 9.998881e-01, -7.997231e-01], + [0, 0, 0, 1] + ]) + + T_velo_to_cam = np.array([[7.533745e-03, -9.999714e-01, -6.166020e-04, -4.069766e-03], + [1.480249e-02, 7.280733e-04, -9.998902e-01, -7.631618e-02], + [9.998621e-01, 7.523790e-03, 1.480755e-02, -2.717806e-01], + [0, 0, 0, 1] + ]) + + T_cam_to_cam_center = np.array([[1.0, 0.0, 0.0, -0.537 / 2.0], + [0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0] + ]) + + T_imu_to_cam = T_velo_to_cam.dot(T_imu_to_velo) + + T_imu_to_cam_center = T_cam_to_cam_center.dot(T_imu_to_cam) + + T_cam_center_to_imu = np.linalg.inv(T_imu_to_cam_center) + + # initialization offset: The ground truth data of the vehicle starts with (0, 0, 0) in GPS/IMU frame. But in the VO estimation, we set initial position of the stereo-camera's center as (0,0,0) using initial r. Therefore, we convert the ground truth data to camera center position in GPS frame with (0, 0, 0) as the initial position. The convertion is done by translation vector t_cv_v. + + # translation from vehicle frame to camera center frame expressed in vehicle frame (from Kitti website) + t_cv_v = np.array([1.09, -0.32 - 0.537 / 2.0, 0.8]) + + cam_center_gt = np.zeros((sequence_num, 3)) + for idx in range(sequence_num): + cam_center_gt[idx, :] = pose_gt[idx, 0:3, 3] + t_cv_v + + # Transformation in vehicle frame + T_vehicle = np.zeros((sequence_num, 4, 4)) + + # record video + os.chdir(cwd) + fourcc = cv.VideoWriter_fourcc(*'MPEG') + video = cv.VideoWriter('./video.avi', fourcc, 5.0, (1242, 775)) # 375*2 + 25 (margin) + + for img_id in range(sequence_num): + img_left = cv.imread(cwd + path_0 + str(img_id).zfill(zero_num) + '.png', 0) + img_right = cv.imread(cwd + path_1 + str(img_id).zfill(zero_num) + '.png', 0) + + # finite-state machine + # update the C and r + frame_left, frame_right = vo.update(img_left, img_right, img_id) + + # Create a white margin between two frames + margin = np.ones_like(frame_left) * 255 + vertical_frame = np.concatenate((frame_left, margin[0:25], frame_right), axis=0) + + # Update the transformation matrix + T_update = np.vstack(((np.concatenate((vo.C, (vo.r).reshape(-1, 1)), axis=1)), + np.array([[0, 0, 0, 1]]))) + T = T_update.dot(T) + # Store the history of transformation matrix + T_hist[img_id] = T + # convert to vehicle frame + T_vehicle[img_id] = T_cam_center_to_imu.dot(np.linalg.inv(T_hist[img_id])) + + cv.imshow('Visual Odometry', vertical_frame) + video.write(vertical_frame) + if cv.waitKey(10) & 0xFF == ord('q'): + break + + print("VO ends\n") + video.release() + cv.destroyAllWindows() + + # save the estimated transformation matrix + np.save('VO_T.npy', T_vehicle) + + fig_traj = plt.figure(facecolor="white") + ax_t = fig_traj.add_subplot(111, projection='3d') + ax_t.plot(cam_center_gt[:, 0], cam_center_gt[:, 1], cam_center_gt[:, 2], color='red', label='ground truth trajectory', linewidth=1.9, alpha=0.9) + ax_t.plot(T_vehicle[:, 0, 3], T_vehicle[:, 1, 3], T_vehicle[:, 2, 3], color='steelblue', label='VO trajectory', linewidth=1.9, alpha=0.9) + ax_t.set_xlabel(r'X [m]') + ax_t.set_ylabel(r'Y [m]') + ax_t.set_zlabel(r'Z [m]') + ax_t.legend() + ax_t.set_zlim3d(-20.0, 25.0) + plt.title(r"Trajectory of the vehicle", fontsize=13, fontweight=0, color='black', style='italic', y=1.02) + plt.show() + + +if __name__ == '__main__': + print('We are using OpenCV version {}'.format(cv.__version__)) + main() diff --git a/aer-course-project/Lab4/stereo_vo_base.py b/aer-course-project/Lab4/stereo_vo_base.py new file mode 100644 index 000000000..89db99006 --- /dev/null +++ b/aer-course-project/Lab4/stereo_vo_base.py @@ -0,0 +1,462 @@ +""" +2021-02 -- Wenda Zhao, Miller Tang + +This is the class for a steoro visual odometry designed +for the course AER 1217H, Development of Autonomous UAS +https://carre.utoronto.ca/aer1217 +""" +import numpy as np +import cv2 as cv +import sys +import math + +STAGE_FIRST_FRAME = 0 +STAGE_SECOND_FRAME = 1 +STAGE_DEFAULT_FRAME = 2 + +# np.random.rand(1217) In starter code, probably typo +np.random.seed(1217) + + +class StereoCamera: + def __init__(self, baseline, focalLength, fx, fy, cu, cv): + self.baseline = baseline + self.f_len = focalLength + self.fx = fx + self.fy = fy + self.cu = cu + self.cv = cv + +class VisualOdometry: + def __init__(self, cam): + self.frame_stage = 0 + self.cam = cam + self.new_frame_left = None + self.last_frame_left = None + self.new_frame_right = None + self.last_frame_right = None + self.C = np.eye(3) # current rotation (initiated to be eye matrix) + self.r = np.zeros((3,1)) # current translation (initiated to be zeros) + self.kp_l_prev = None # previous key points (left) + self.des_l_prev = None # previous descriptor for key points (left) + self.kp_r_prev = None # previous key points (right) + self.des_r_prev = None # previoud descriptor key points (right) + self.detector = cv.xfeatures2d.SIFT_create() # using sift for detection + self.feature_color = (255, 191, 0) + self.inlier_color = (32,165,218) + + + def feature_detection(self, img): + kp, des = self.detector.detectAndCompute(img, None) + feature_image = cv.drawKeypoints(img,kp,None) + return kp, des, feature_image + + def featureTracking(self, prev_kp, cur_kp, img, color=(0,255,0), alpha=0.5): + img = cv.cvtColor(img, cv.COLOR_GRAY2BGR) + cover = np.zeros_like(img) + # Draw the feature tracking + for i, (new, old) in enumerate(zip(cur_kp, prev_kp)): + a, b = new.ravel() + c, d = old.ravel() + a,b,c,d = int(a), int(b), int(c), int(d) + cover = cv.line(cover, (a,b), (c,d), color, 2) + cover = cv.circle(cover, (a,b), 3, color, -1) + frame = cv.addWeighted(cover, alpha, img, 0.75, 0) + + return frame + + def find_feature_correspondences(self, kp_l_prev, des_l_prev, kp_r_prev, des_r_prev, kp_l, des_l, kp_r, des_r): + VERTICAL_PX_BUFFER = 1 # buffer for the epipolor constraint in number of pixels + FAR_THRESH = 7 # 7 pixels is approximately 55m away from the camera + CLOSE_THRESH = 65 # 65 pixels is approximately 4.2m away from the camera + + nfeatures = len(kp_l) + bf = cv.BFMatcher(cv.NORM_L2, crossCheck=True) # BFMatcher for SIFT or SURF features matching + + ## using the current left image as the anchor image + match_l_r = bf.match(des_l, des_r) # current left to current right + match_l_l_prev = bf.match(des_l, des_l_prev) # cur left to prev. left + match_l_r_prev = bf.match(des_l, des_r_prev) # cur left to prev. right + + kp_query_idx_l_r = [mat.queryIdx for mat in match_l_r] + kp_query_idx_l_l_prev = [mat.queryIdx for mat in match_l_l_prev] + kp_query_idx_l_r_prev = [mat.queryIdx for mat in match_l_r_prev] + + kp_train_idx_l_r = [mat.trainIdx for mat in match_l_r] + kp_train_idx_l_l_prev = [mat.trainIdx for mat in match_l_l_prev] + kp_train_idx_l_r_prev = [mat.trainIdx for mat in match_l_r_prev] + + ## loop through all the matched features to find common features + features_coor = np.zeros((1,8)) + for pt_idx in np.arange(nfeatures): + if (pt_idx in set(kp_query_idx_l_r)) and (pt_idx in set(kp_query_idx_l_l_prev)) and (pt_idx in set(kp_query_idx_l_r_prev)): + temp_feature = np.zeros((1,8)) + temp_feature[:, 0:2] = kp_l_prev[kp_train_idx_l_l_prev[kp_query_idx_l_l_prev.index(pt_idx)]].pt + temp_feature[:, 2:4] = kp_r_prev[kp_train_idx_l_r_prev[kp_query_idx_l_r_prev.index(pt_idx)]].pt + temp_feature[:, 4:6] = kp_l[pt_idx].pt + temp_feature[:, 6:8] = kp_r[kp_train_idx_l_r[kp_query_idx_l_r.index(pt_idx)]].pt + features_coor = np.vstack((features_coor, temp_feature)) + features_coor = np.delete(features_coor, (0), axis=0) + + ## additional filter to refine the feature coorespondences + # 1. drop those features do NOT follow the epipolar constraint + features_coor = features_coor[ + (np.absolute(features_coor[:,1] - features_coor[:,3]) < VERTICAL_PX_BUFFER) & + (np.absolute(features_coor[:,5] - features_coor[:,7]) < VERTICAL_PX_BUFFER)] + + # 2. drop those features that are either too close or too far from the cameras + features_coor = features_coor[ + (np.absolute(features_coor[:,0] - features_coor[:,2]) > FAR_THRESH) & + (np.absolute(features_coor[:,0] - features_coor[:,2]) < CLOSE_THRESH)] + + features_coor = features_coor[ + (np.absolute(features_coor[:,4] - features_coor[:,6]) > FAR_THRESH) & + (np.absolute(features_coor[:,4] - features_coor[:,6]) < CLOSE_THRESH)] + # features_coor: + # prev_l_x, prev_l_y, prev_r_x, prev_r_y, cur_l_x, cur_l_y, cur_r_x, cur_r_y + return features_coor + + ### POINT CLOUD ALIGNMENT ################################### + + def compute_transformation_matrix(self, points_before, points_after): + """ + ... + + Parameters: + points_before : numpy.ndarray size: (?, 3) either (M x 3) or (3 x 3) + points_after : numpy.ndarray size: (?, 3) either (M x 3) or (3 x 3) + ... + + Returns: + T : numpy.ndarray size: (4, 4) + ... + """ + # TODO: + # each input array is ? x 3 (X, Y, Z) + # compute transform T from array + T = np.eye(4) + return T + + + + ## RANSAC ################################################## + #TODO: - Determine good INLIER_THRESHOLD_EUCLIDEAN value in filter_inliers_RASAC fxn + # - Determine good HIGH_INLIER_PERC_THRESHOLD value in compute_max_iter_RANSAC fxn + # - Determine good perc_outlier value in compute_max_iter_RANSAC fxn + # - Debug + + def convert_features_to_3D(self, features_coor): + """ + Take the u and v pixel information (x and y) of the left and right camera from the previous and current + frames and produce the 3D point cloud points for the previous frame and current frame, independently. + + Parameters: + features_coor : list + The features_coor is of size N x 8 where each row is the detected feature and the columns follow + the following order: + [prev_l_x, prev_l_y, prev_r_x, prev_r_y, cur_l_x, cur_l_y, cur_r_x, cur_r_y] + + Returns: + p_before: numpy.ndarray + p_cur: numpy.ndarray + Each array is of size N x 3 and represent the 3D coordinates of each detected feature, where each array + represents the respective timeframe. + """ + + #assuming epipolar from starter code, only look at x coords + f_prev = features_coor[:, :4] #N x 4 + f_cur = features_coor[:, 4:8] #N x 4 + + def inverse_stereo(self, l_and_r_feats): #input N x 4 (l_u, l_v, r_u, r_l) + """ + Convert features from left and right camera to 3d points using the inverse stereo model + + Parameters: + features_coor : list + The features_coor is of size N x 4 where each row is the detected feature and the columns follow + the following order: + [l_u, l_v, r_u, r_v] + Returns: + numpy.ndarray + An array of size N x 3 representing the 3D coordinates of the features + """ + l_u = l_and_r_feats[:, 0] + disparity = l_u - l_and_r_feats[:, 2] #assumes epipolar from starter code filtering and rectification, l_u - r_u + Z = np.array(self.cam.baseline * self.cam.f_len / disparity) #N, + X = np.array(Z * (l_u - self.cam.cu) / self.cam.f_len) #N, + Y = np.array(Z * (l_and_r_feats[:, 1] - self.cam.cv) / self.cam.f_len) #N, + points_3D = np.column_stack((X, Y, Z)) + return points_3D # N x 3 (x, y, z) + + p_before = inverse_stereo(self, f_prev) + p_cur = inverse_stereo(self, f_cur) + return p_before, p_cur #each N x 3 + + def compute_max_iter_RANSAC(self, certainty): + """ + Use the RANSAC eqn to determine a max number of iterations to perform to gain a degree of certainty in + inlier detection. max_iter = frac{\log(1 - p)}{\log(1 - (1-e)^s)}, where p is the certainty, e + is the estimated proportion of outliers, and s is the number of samples needed to estimate the model. + + Parameters: + certainty : float + value from 0 - 1 (non-inclusive) of how certain we want to be. Usually set to 0.99 or higher + + Returns: + int + The number of iterations required to achieve the desired certainty + """ + pts_picked = 3 + perc_outlier = 0.3 ## CHOSEN ARBITRARILY, CONSIDER REVISING#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + N = math.log(1 - certainty) / math.log(1 - (1 - perc_outlier) ** pts_picked) + return math.ceil(N) + + def calculate_euclid(self, p_cur, p_transformed): + """ + Determine the Euclidean distance of each feature between the current frame, and the transformation + applied to the previous frame. + + Parameters: + p_cur : numpy.ndarray size: (N, 3) + p_transformed : numpy.ndarray size: (N, 3) + 2 supposedly similar frames of features. + + Returns: + numpy.ndarray size: (N,) + Each element represents the Euclidean distance for that row between the 2 frames + """ + squared_diff = (p_cur - p_transformed) ** 2 + sum_squared_diff = np.sum(squared_diff, axis=1) + dist = np.sqrt(sum_squared_diff) + return dist + + def filter_inliers_RASAC(self, p_before, p_cur, max_iter): + """ + Apply RANSAC inlier filtering to the given data to only preserve the features that are inliers. + + Parameters: + p_before : numpy.ndarray size: (N, 3) + p_cur : numpy.ndarray size: (N, 3) + max_iter : int + + The np arrays are each the unfiltered 3D point clouds of the features from the previous and current frame. + max_iter is the number of iterations of RANSAC required to achieve a certain confidence level (0.99). + + Returns: + p_a : numpy.ndarray size: (M, 3) + p_b : numpy.ndarray size: (M, 3) + inlier_indices : numpy.ndarray size: (M,) + + N >= M. The returned arrays are the 3D point clouds of the inlier features for the previous and current + frames, respectively. The elements of inlier_indices represent the indices of N that were considered inliers. + """ + + max_inliners = -1 + top_T = np.zeros((4,4)) + INLIER_THRESHOLD_EUCLIDEAN = 10 # metres CONSIDER REVISING !!!!!!!!!!!!!!!!!!!!!!!!! + HIGH_INLIER_PERC_THRESHOLD = 0.95 #more than HIGH_INLIER_PERC_THRESHOLD percent of points are inliers, can break RANSAC + N = p_before.shape[0] + + def inliers_from_T(T, p_before, p_cur): + """ + Given a certain transform T, apply that transform to the previous frame pointcloud and determine + the inliers by comparing the euclidean distance to the current frame pointcloud. + + Parameters: + T : numpy.ndarray size: (4, 4) + p_before : numpy.ndarray size: (N, 3) + p_cur : numpy.ndarray size: (N, 3) + + The np arrays are each the unfiltered 3D point clouds of the features from the previous and current frame. + + Returns: + inliers_cnt : int value <= N + inlier_indices : numpy.ndarray size: (inliers_cnt,) and each element is value from 0-> N-1 + + Returns the number of inliers found, as well as the indices of those inliers w.r.t. the original + N features. + """ + p_before_T = np.hstack((p_before, np.ones((N, 1)))).T #4 x N where last row is 1s + + p_transformed_T = T @ p_before_T # 4x4 @ 4 x N => 4 x N + p_transformed = p_transformed_T[:3, :].T #N x 3 X, Y, Z + + dist = self.calculate_euclid(p_cur, p_transformed) #N, + mask = dist < INLIER_THRESHOLD_EUCLIDEAN #Boolean len N + inlier_indices = np.where(mask)[0] # M, + inliers_cnt = np.count_nonzero(mask) #int + + return inliers_cnt, inlier_indices + + def one_iter_RANSAC(p_before, p_cur): + """ + Run a single iteration of RANSAC using 3 random feature pairs from the previous and current frames. + + Parameters: + p_before : numpy.ndarray size: (N, 3) + p_cur : numpy.ndarray size: (N, 3) + The np arrays are each the unfiltered 3D point clouds of the features from the previous and current frame. + + Returns: + temp_T : numpy.ndarray size: (4, 4) + inliers_cnt : int value <= N + + Returns the transform that was applied to the previous frame, as well as the number of inliers found + as a result of the transformation. + """ + rand_idx = np.random.choice(N, 3, replace=False) #3 random samples + prev_test_points = p_before[rand_idx] + cur_test_points = p_cur[rand_idx] + temp_T = self.compute_transformation_matrix(prev_test_points, cur_test_points) + inliers_cnt, _ = inliers_from_T(temp_T, p_before, p_cur) + return temp_T, inliers_cnt + + for _ in range(max_iter): + temp_T, inliers_cnt = one_iter_RANSAC(p_before, p_cur) + if inliers_cnt > max_inliners: + max_inliners = inliers_cnt + top_T = temp_T + if (inliers_cnt / N) > HIGH_INLIER_PERC_THRESHOLD: + break + + _, inlier_indices = inliers_from_T(top_T, p_before, p_cur) #repeat best transform + + p_a = p_before[inlier_indices] + p_b = p_cur[inlier_indices] + + return p_a, p_b, inlier_indices + + + + ## POSE ESTIMATION ################################################ + + def pose_estimation(self, features_coor): + """ + Process the detected features of the left and right camera in the previous and current frames as 3D + pointclouds, identify the features that are inliers, and use those features to compute the rotation + and translation from the previous frame to the current frame. + + Parameters: + features_coor : list + The features_coor is of size N x 8 where each row is the detected feature and the columns follow + the following order: + [prev_l_x, prev_l_y, prev_r_x, prev_r_y, cur_l_x, cur_l_y, cur_r_x, cur_r_y] + + Returns: + C : numpy.ndarray size: (3, 3) + r : numpy.ndarray size: (3,) + f_r_prev : list size: (M, 2) + f_r_cur : list size: (M, 2) + + C and r are the translations applied to the previous frame to convert them to the current_frame. + f_r_prev are the filtered features for the right camera in the previous frame, and f_r_cur are + the filtered features for the right camera in the current frame. + """ + + + # ------------- start your code here -------------- # + + #1. Convert to 3d pointcloud points. 2 sets before: [x, y, z], and current: [x, y, z] + #2. Iteratively run RANSAC to get inliers by fitting C and r to 3 points + #3. Choose version that had the most inliers + #3. Use M features for cloud alignment + p_before, p_cur = self.convert_features_to_3D(features_coor) #np arrays N x 3 each + certainty = 0.99 + max_iter = self.compute_max_iter_RANSAC(certainty) + p_a, p_b, inlier_indices = self.filter_inliers_RASAC(p_before, p_cur, max_iter) #M x 3 arrays + + + #TODO: Compute C and r from p_a and p_b which are inlier points + T = self.compute_transformation_matrix(p_a, p_b) + C = T[:3, :3] + r = T[:3, 3] + + + filtered_features_coor = features_coor[inlier_indices] + f_r_prev, f_r_cur = filtered_features_coor[:,2:4], filtered_features_coor[:,6:8] + return C, r, f_r_prev, f_r_cur + + def processFirstFrame(self, img_left, img_right): + kp_l, des_l, feature_l_img = self.feature_detection(img_left) + kp_r, des_r, feature_r_img = self.feature_detection(img_right) + + self.kp_l_prev = kp_l + self.des_l_prev = des_l + self.kp_r_prev = kp_r + self.des_r_prev = des_r + + self.frame_stage = STAGE_SECOND_FRAME + return img_left, img_right + + def processSecondFrame(self, img_left, img_right): + kp_l, des_l, feature_l_img = self.feature_detection(img_left) + kp_r, des_r, feature_r_img = self.feature_detection(img_right) + + # compute feature correspondance + features_coor = self.find_feature_correspondences(self.kp_l_prev, self.des_l_prev, + self.kp_r_prev, self.des_r_prev, + kp_l, des_l, kp_r, des_r) #Nx8 + # draw the feature tracking on the left img + img_l_tracking = self.featureTracking(features_coor[:,0:2], features_coor[:,4:6],img_left, color = self.feature_color) + + # lab4 assignment: compute the vehicle pose + [self.C, self.r, f_r_prev, f_r_cur] = self.pose_estimation(features_coor) + + # draw the feature (inliers) tracking on the right img + img_r_tracking = self.featureTracking(f_r_prev, f_r_cur, img_right, color = self.inlier_color, alpha=1.0) + + # update the key point features on both images + self.kp_l_prev = kp_l + self.des_l_prev = des_l + self.kp_r_prev = kp_r + self.des_r_prev = des_r + self.frame_stage = STAGE_DEFAULT_FRAME + + return img_l_tracking, img_r_tracking + + def processFrame(self, img_left, img_right, frame_id): + kp_l, des_l, feature_l_img = self.feature_detection(img_left) + + kp_r, des_r, feature_r_img = self.feature_detection(img_right) + + # compute feature correspondance + features_coor = self.find_feature_correspondences(self.kp_l_prev, self.des_l_prev, + self.kp_r_prev, self.des_r_prev, + kp_l, des_l, kp_r, des_r) #N x 8 + # draw the feature tracking on the left img + img_l_tracking = self.featureTracking(features_coor[:,0:2], features_coor[:,4:6], img_left, color = self.feature_color) + + # lab4 assignment: compute the vehicle pose + [self.C, self.r, f_r_prev, f_r_cur] = self.pose_estimation(features_coor) + + # draw the feature (inliers) tracking on the right img + img_r_tracking = self.featureTracking(f_r_prev, f_r_cur, img_right, color = self.inlier_color, alpha=1.0) + + # update the key point features on both images + self.kp_l_prev = kp_l + self.des_l_prev = des_l + self.kp_r_prev = kp_r + self.des_r_prev = des_r + + return img_l_tracking, img_r_tracking + + def update(self, img_left, img_right, frame_id): + + self.new_frame_left = img_left + self.new_frame_right = img_right + + if(self.frame_stage == STAGE_DEFAULT_FRAME): + frame_left, frame_right = self.processFrame(img_left, img_right, frame_id) + + elif(self.frame_stage == STAGE_SECOND_FRAME): + frame_left, frame_right = self.processSecondFrame(img_left, img_right) + + elif(self.frame_stage == STAGE_FIRST_FRAME): + frame_left, frame_right = self.processFirstFrame(img_left, img_right) + + self.last_frame_left = self.new_frame_left + self.last_frame_right= self.new_frame_right + + return frame_left, frame_right + + diff --git a/examples/cbf/cbf_experiment.py b/examples/cbf/cbf_experiment.py new file mode 100644 index 000000000..e6a73b754 --- /dev/null +++ b/examples/cbf/cbf_experiment.py @@ -0,0 +1,118 @@ +'''This script tests the CBF safety filter implementation.''' + +import os +import shutil +from functools import partial + +import matplotlib.pyplot as plt +import numpy as np + +from safe_control_gym.experiments.base_experiment import BaseExperiment +from safe_control_gym.utils.configuration import ConfigFactory +from safe_control_gym.utils.registration import make + + +def run(plot=True, training=True, n_episodes=1, n_steps=None, curr_path='.', save_data=False): + '''The main function running CBF experiments. + + Args: + plot (bool): Whether to plot graphs. + training (bool): Whether to train the CBF. + n_episodes (int): The number of episodes to execute. + n_steps (int): The total number of steps to execute. + curr_path (str): The current relative path to the experiment folder. + ''' + + # Create the configuration dictionary. + fac = ConfigFactory() + config = fac.merge() + + # Create an environment + env_func = partial(make, + config.task, + **config.task_config) + + env = env_func() + + # Setup controller. + ctrl = make(config.algo, + env_func, + **config.algo_config, + output_dir=curr_path + '/temp') + + # Load state_dict from trained model. + model_dir = os.path.dirname(os.path.abspath(__file__)) + '/models' + ctrl.load(os.path.join(model_dir, f'{config.algo}_model_{config.task}.pt')) + + # Run without safety filter + experiment = BaseExperiment(env, ctrl) + results, _ = experiment.run_evaluation(n_episodes=n_episodes, n_steps=n_steps) + + # Setup CBF. + safety_filter = make(config.safety_filter, + env_func, + **config.sf_config, + output_dir=curr_path + '/temp') + safety_filter.reset() + + if training is True and config.safety_filter == 'cbf_nn': + train_env = env_func(init_state=None, randomized_init=True) + safety_filter.uncertified_controller = ctrl + safety_filter.learn(env=train_env) + if save_data is True: + safety_filter.save(path=curr_path + f'/models/{config.safety_filter}_{config.algo}_model_{config.task}.pt') + else: + safety_filter.load(path=curr_path + f'/models/{config.safety_filter}_{config.algo}_model_{config.task}.pt') + + # Remove temporary files and directories + shutil.rmtree(os.path.dirname(os.path.abspath(__file__)) + '/temp', ignore_errors=True) + + # Run with safety filter + experiment = BaseExperiment(env, ctrl, safety_filter=safety_filter) + certified_results, _ = experiment.run_evaluation(n_episodes=n_episodes, n_steps=n_steps) + ctrl.close() + cbf_results = certified_results['safety_filter_data'][0] + safety_filter.close() + + corrections = cbf_results['correction'][0] > 1e-6 + corrections = np.append(corrections, False) + + # Plot Results + if plot is True: + _, ax_obs = plt.subplots() + ax_obs.plot(certified_results['obs'][0][:, 0], certified_results['obs'][0][:, 2], '.-', label='Certified') + ax_obs.plot(results['obs'][0][:10, 0], results['obs'][0][:10, 2], 'r--', label='Uncertified') + ax_obs.plot(certified_results['obs'][0][corrections, 0], certified_results['obs'][0][corrections, 2], 'r.', label='Modified') + ax_obs.legend() + ax_obs.set_title('State Space') + ax_obs.set_xlabel(r'$x$') + ax_obs.set_ylabel(r'$\theta$') + ax_obs.set_box_aspect(0.5) + + _, ax_act = plt.subplots() + ax_act.plot(certified_results['action'][0][:], 'b-', label='Certified Inputs') + ax_act.plot(cbf_results['uncertified_action'][0][:], 'r--', label='Uncertified Input') + ax_act.legend() + ax_act.set_title('Input comparison') + ax_act.set_xlabel('Step') + ax_act.set_ylabel('Input') + ax_act.set_box_aspect(0.5) + + _, ax = plt.subplots() + ax.plot(certified_results['obs'][0][:, 2], certified_results['obs'][0][:, 3], '.-', label='Certified') + ax.plot(certified_results['obs'][0][corrections, 2], certified_results['obs'][0][corrections, 3], 'r.', label='Modified') + uncert_end = results['obs'][0].shape[0] + ax.plot(results['obs'][0][:uncert_end, 2], results['obs'][0][:uncert_end, 3], 'r--', label='Uncertified') + ax.axvline(x=-0.2, color='r', label='Limit') + ax.axvline(x=0.2, color='r') + ax.set_xlabel(r'$\theta$') + ax.set_ylabel(r'$\dot{\theta}$') + ax.set_box_aspect(0.5) + ax.legend() + + plt.tight_layout() + plt.show() + + +if __name__ == '__main__': + run() diff --git a/examples/cbf/cbf_experiment.sh b/examples/cbf/cbf_experiment.sh new file mode 100755 index 000000000..432d4b83d --- /dev/null +++ b/examples/cbf/cbf_experiment.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +TASK='cartpole' + +ALGO='ppo' +# ALGO='sac' + +SAFETY_FILTER='cbf' +# SAFETY_FILTER='cbf_nn' + +# Model-predictive safety certification of an unsafe controller. +python3 ./cbf_experiment.py --task ${TASK} --algo ${ALGO} --safety_filter ${SAFETY_FILTER} --overrides ./config_overrides/${TASK}_config.yaml ./config_overrides/${ALGO}_config.yaml ./config_overrides/${SAFETY_FILTER}_config.yaml diff --git a/examples/cbf/config_overrides/cartpole_config.yaml b/examples/cbf/config_overrides/cartpole_config.yaml new file mode 100644 index 000000000..ba1e1c7f5 --- /dev/null +++ b/examples/cbf/config_overrides/cartpole_config.yaml @@ -0,0 +1,56 @@ +task_config: + seed: 42 + ctrl_freq: 25 + pyb_freq: 1000 + info_in_reset: True + + cost: quadratic + task: stabilization + task_info: + stabilization_goal: [0, 0] + stabilization_goal_tolerance: 0.05 + + # state initialization + init_state: + init_x: 0.4 + init_x_dot: -0.4 + init_theta: -0.18 + init_theta_dot: -0.4 + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -0.5 + high: 0.5 + init_x_dot: + distrib: 'uniform' + low: -0.5 + high: 0.5 + init_theta: + distrib: 'uniform' + low: -0.15 + high: 0.15 + init_theta_dot: + distrib: 'uniform' + low: -0.5 + high: 0.5 + randomized_init: False + + # constraints + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 0.2 + - 2 + lower_bounds: + - -2 + - -2 + - -0.2 + - -2 + - constraint_form: default_constraint + constrained_variable: input + + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/cbf/config_overrides/cbf_config.yaml b/examples/cbf/config_overrides/cbf_config.yaml new file mode 100644 index 000000000..9d0244bbf --- /dev/null +++ b/examples/cbf/config_overrides/cbf_config.yaml @@ -0,0 +1,12 @@ +safety_filter: cbf +sf_config: + slope: 0.1 + soft_constrained: True + slack_weight: 10000.0 + slack_tolerance: 1.0E-3 + + # Prior info + prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null diff --git a/examples/cbf/config_overrides/cbf_nn_config.yaml b/examples/cbf/config_overrides/cbf_nn_config.yaml new file mode 100644 index 000000000..60db5146c --- /dev/null +++ b/examples/cbf/config_overrides/cbf_nn_config.yaml @@ -0,0 +1,23 @@ +safety_filter: cbf_nn +sf_config: + slope: 0.1 + soft_constrained: True + slack_weight: 10000.0 + slack_tolerance: 1.0E-3 + + # NN Parameters + max_num_steps: 250 + + hidden_dims: [256, 256] + learning_rate: 0.001 + + num_episodes: 20 + max_buffer_size: 1.0E+6 + train_batch_size: 64 + train_iterations: 200 + + # Prior info + prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null diff --git a/examples/cbf/config_overrides/ppo_config.yaml b/examples/cbf/config_overrides/ppo_config.yaml new file mode 100644 index 000000000..838c16d5c --- /dev/null +++ b/examples/cbf/config_overrides/ppo_config.yaml @@ -0,0 +1,40 @@ +algo: ppo +algo_config: + # model args + hidden_dim: 64 + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # loss args + gamma: 0.99 + use_gae: True + gae_lambda: 0.95 + use_clipped_value: False + clip_param: 0.2 + target_kl: 0.01 + entropy_coef: 0.01 + + # optim args + opt_epochs: 10 + mini_batch_size: 64 + actor_lr: 0.0003 + critic_lr: 0.001 + max_grad_norm: 0.5 + + # runner args + max_env_steps: 100000 + num_workers: 1 + rollout_batch_size: 4 + rollout_steps: 100 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 1000 + save_interval: 1000 + num_checkpoints: 0 + eval_interval: 1000 + eval_save_best: True + tensorboard: False diff --git a/examples/cbf/models/cbf_nn_ppo_model_cartpole.pt b/examples/cbf/models/cbf_nn_ppo_model_cartpole.pt new file mode 100644 index 000000000..c90398060 Binary files /dev/null and b/examples/cbf/models/cbf_nn_ppo_model_cartpole.pt differ diff --git a/examples/cbf/models/cbf_nn_sac_model_cartpole.pt b/examples/cbf/models/cbf_nn_sac_model_cartpole.pt new file mode 100644 index 000000000..3e5f837ef Binary files /dev/null and b/examples/cbf/models/cbf_nn_sac_model_cartpole.pt differ diff --git a/examples/cbf/models/sac_model_cartpole.pt b/examples/cbf/models/sac_model_cartpole.pt new file mode 100644 index 000000000..0ad4e8f56 Binary files /dev/null and b/examples/cbf/models/sac_model_cartpole.pt differ diff --git a/examples/lqr/config_overrides/cartpole/cartpole_stabilization.yaml b/examples/lqr/config_overrides/cartpole/cartpole_stabilization.yaml new file mode 100644 index 000000000..0cbb7226e --- /dev/null +++ b/examples/lqr/config_overrides/cartpole/cartpole_stabilization.yaml @@ -0,0 +1,33 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 15 + pyb_freq: 750 + physics: pyb + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -2 + high: 2 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + + task: stabilization + task_info: + stabilization_goal: [1.0, 0.0] + stabilization_goal_tolerance: 0.0 + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/lqr/config_overrides/cartpole/cartpole_tracking.yaml b/examples/lqr/config_overrides/cartpole/cartpole_tracking.yaml new file mode 100644 index 000000000..7eefd0fb0 --- /dev/null +++ b/examples/lqr/config_overrides/cartpole/cartpole_tracking.yaml @@ -0,0 +1,36 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 15 + pyb_freq: 750 + physics: pyb + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + + task: traj_tracking + task_info: + trajectory_type: 'circle' + num_cycles: 2 + trajectory_plane: 'zx' + trajectory_position_offset: [0, 0] + trajectory_scale: 1 + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/lqr/config_overrides/cartpole/ilqr_cartpole_stabilization.yaml b/examples/lqr/config_overrides/cartpole/ilqr_cartpole_stabilization.yaml new file mode 100644 index 000000000..46a7df26c --- /dev/null +++ b/examples/lqr/config_overrides/cartpole/ilqr_cartpole_stabilization.yaml @@ -0,0 +1,16 @@ +algo: ilqr +algo_config: + # Cost parameters + q_lqr: [1, 1, 1, 1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True + + # iLQR arguments + max_iterations: 15 + lamb_factor: 10 + lamb_max: 1000 + epsilon: 0.01 diff --git a/examples/lqr/config_overrides/cartpole/ilqr_cartpole_tracking.yaml b/examples/lqr/config_overrides/cartpole/ilqr_cartpole_tracking.yaml new file mode 100644 index 000000000..bfe07e1b9 --- /dev/null +++ b/examples/lqr/config_overrides/cartpole/ilqr_cartpole_tracking.yaml @@ -0,0 +1,16 @@ +algo: ilqr +algo_config: + # Cost parameters + q_lqr: [1, 0.1, 0.1, 0.1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True + + # iLQR arguments + max_iterations: 15 + lamb_factor: 10 + lamb_max: 1000 + epsilon: 0.01 diff --git a/examples/lqr/config_overrides/cartpole/lqr_cartpole_stabilization.yaml b/examples/lqr/config_overrides/cartpole/lqr_cartpole_stabilization.yaml new file mode 100644 index 000000000..93688a792 --- /dev/null +++ b/examples/lqr/config_overrides/cartpole/lqr_cartpole_stabilization.yaml @@ -0,0 +1,10 @@ +algo: lqr +algo_config: + # Cost parameters + q_lqr: [1, 1, 1, 1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True diff --git a/examples/lqr/config_overrides/cartpole/lqr_cartpole_tracking.yaml b/examples/lqr/config_overrides/cartpole/lqr_cartpole_tracking.yaml new file mode 100644 index 000000000..8f3a6071a --- /dev/null +++ b/examples/lqr/config_overrides/cartpole/lqr_cartpole_tracking.yaml @@ -0,0 +1,10 @@ +algo: lqr +algo_config: + # Cost parameters + q_lqr: [1, 0.1, 0.1, 0.1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True diff --git a/examples/lqr/config_overrides/quadrotor_2D/ilqr_quadrotor_2D_stabilization.yaml b/examples/lqr/config_overrides/quadrotor_2D/ilqr_quadrotor_2D_stabilization.yaml new file mode 100644 index 000000000..92d982dc2 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_2D/ilqr_quadrotor_2D_stabilization.yaml @@ -0,0 +1,16 @@ +algo: ilqr +algo_config: + # Cost parameters + q_lqr: [1, 1, 1, 1, 1, 1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True + + # iLQR arguments + max_iterations: 15 + lamb_factor: 10 + lamb_max: 1000 + epsilon: 0.01 diff --git a/examples/lqr/config_overrides/quadrotor_2D/ilqr_quadrotor_2D_tracking.yaml b/examples/lqr/config_overrides/quadrotor_2D/ilqr_quadrotor_2D_tracking.yaml new file mode 100644 index 000000000..e66488f76 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_2D/ilqr_quadrotor_2D_tracking.yaml @@ -0,0 +1,16 @@ +algo: ilqr +algo_config: + # Cost parameters + q_lqr: [1, 0.1, 1, 0.1, 0.1, 0.1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True + + # iLQR arguments + max_iterations: 15 + lamb_factor: 10 + lamb_max: 1000 + epsilon: 0.01 diff --git a/examples/lqr/config_overrides/quadrotor_2D/lqr_quadrotor_2D_stabilization.yaml b/examples/lqr/config_overrides/quadrotor_2D/lqr_quadrotor_2D_stabilization.yaml new file mode 100644 index 000000000..b12613426 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_2D/lqr_quadrotor_2D_stabilization.yaml @@ -0,0 +1,10 @@ +algo: lqr +algo_config: + # Cost parameters + q_lqr: [1, 1, 1, 1, 1, 1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True diff --git a/examples/lqr/config_overrides/quadrotor_2D/lqr_quadrotor_2D_tracking.yaml b/examples/lqr/config_overrides/quadrotor_2D/lqr_quadrotor_2D_tracking.yaml new file mode 100644 index 000000000..f28d72845 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_2D/lqr_quadrotor_2D_tracking.yaml @@ -0,0 +1,10 @@ +algo: lqr +algo_config: + # Cost parameters + q_lqr: [1, 0.1, 1, 0.1, 0.1, 0.1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True diff --git a/examples/lqr/config_overrides/quadrotor_2D/quadrotor_2D_stabilization.yaml b/examples/lqr/config_overrides/quadrotor_2D/quadrotor_2D_stabilization.yaml new file mode 100644 index 000000000..134575710 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_2D/quadrotor_2D_stabilization.yaml @@ -0,0 +1,45 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 2 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.5 + high: 1.5 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: stabilization + task_info: + stabilization_goal: [0, 1] + stabilization_goal_tolerance: 0.0 + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/lqr/config_overrides/quadrotor_2D/quadrotor_2D_tracking.yaml b/examples/lqr/config_overrides/quadrotor_2D/quadrotor_2D_tracking.yaml new file mode 100644 index 000000000..aa68b6912 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_2D/quadrotor_2D_tracking.yaml @@ -0,0 +1,48 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 2 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.5 + high: 1.5 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: traj_tracking + task_info: + trajectory_type: figure8 + num_cycles: 1 + trajectory_plane: 'xz' + trajectory_position_offset: [0, 1] + trajectory_scale: 0.75 + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/lqr/config_overrides/quadrotor_3D/ilqr_quadrotor_3D_stabilization.yaml b/examples/lqr/config_overrides/quadrotor_3D/ilqr_quadrotor_3D_stabilization.yaml new file mode 100644 index 000000000..dbea3b4bd --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_3D/ilqr_quadrotor_3D_stabilization.yaml @@ -0,0 +1,16 @@ +algo: ilqr +algo_config: + # Cost parameters + q_lqr: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True + + # iLQR arguments + max_iterations: 15 + lamb_factor: 10 + lamb_max: 1000 + epsilon: 0.01 diff --git a/examples/lqr/config_overrides/quadrotor_3D/ilqr_quadrotor_3D_tracking.yaml b/examples/lqr/config_overrides/quadrotor_3D/ilqr_quadrotor_3D_tracking.yaml new file mode 100644 index 000000000..df2510885 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_3D/ilqr_quadrotor_3D_tracking.yaml @@ -0,0 +1,16 @@ +algo: ilqr +algo_config: + # Cost parameters + q_lqr: [1, 0.1, 1, 0.1, 1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True + + # iLQR arguments + max_iterations: 15 + lamb_factor: 10 + lamb_max: 1000 + epsilon: 0.01 diff --git a/examples/lqr/config_overrides/quadrotor_3D/lqr_quadrotor_3D_stabilization.yaml b/examples/lqr/config_overrides/quadrotor_3D/lqr_quadrotor_3D_stabilization.yaml new file mode 100644 index 000000000..cca5a98aa --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_3D/lqr_quadrotor_3D_stabilization.yaml @@ -0,0 +1,10 @@ +algo: lqr +algo_config: + # Cost parameters + q_lqr: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True diff --git a/examples/lqr/config_overrides/quadrotor_3D/lqr_quadrotor_3D_tracking.yaml b/examples/lqr/config_overrides/quadrotor_3D/lqr_quadrotor_3D_tracking.yaml new file mode 100644 index 000000000..18b875ea5 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_3D/lqr_quadrotor_3D_tracking.yaml @@ -0,0 +1,10 @@ +algo: lqr +algo_config: + # Cost parameters + q_lqr: [1, 0.1, 1, 0.1, 1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + r_lqr: [0.1] + + # Model arguments + # Note: Higher simulation frequency is required if using controller designed + # based on the continuous-time model + discrete_dynamics: True diff --git a/examples/lqr/config_overrides/quadrotor_3D/quadrotor_3D_stabilization.yaml b/examples/lqr/config_overrides/quadrotor_3D/quadrotor_3D_stabilization.yaml new file mode 100644 index 000000000..8ebffccb7 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_3D/quadrotor_3D_stabilization.yaml @@ -0,0 +1,71 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 3 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_y: + distrib: 'uniform' + low: -1 + high: 1 + init_y_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.5 + high: 1.5 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_q: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_r: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: stabilization + task_info: + stabilization_goal: [0, 0, 1] + stabilization_goal_tolerance: 0.0 + proj_point: [0, 0, 0.5] + proj_normal: [0, 1, 1] + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/lqr/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml b/examples/lqr/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml new file mode 100644 index 000000000..4089e5359 --- /dev/null +++ b/examples/lqr/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml @@ -0,0 +1,74 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 3 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_y: + distrib: 'uniform' + low: -1 + high: 1 + init_y_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.5 + high: 1.5 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_q: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_r: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: traj_tracking + task_info: + trajectory_type: figure8 + num_cycles: 1 + trajectory_plane: 'xz' + trajectory_position_offset: [0, 1] + trajectory_scale: 0.75 + proj_point: [0, 0, 0.5] + proj_normal: [0, 1, 1] + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/lqr/lqr_experiment.py b/examples/lqr/lqr_experiment.py new file mode 100644 index 000000000..4b6b46d86 --- /dev/null +++ b/examples/lqr/lqr_experiment.py @@ -0,0 +1,151 @@ +'''A LQR and iLQR example.''' + +import os +import pickle +from collections import defaultdict +from functools import partial + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.ticker import FormatStrFormatter + +from safe_control_gym.envs.benchmark_env import Task +from safe_control_gym.experiments.base_experiment import BaseExperiment +from safe_control_gym.utils.configuration import ConfigFactory +from safe_control_gym.utils.registration import make + + +def run(gui=True, n_episodes=1, n_steps=None, save_data=False): + '''The main function running LQR and iLQR experiments. + + Args: + gui (bool): Whether to display the gui and plot graphs. + n_episodes (int): The number of episodes to execute. + n_steps (int): The total number of steps to execute. + save_data (bool): Whether to save the collected experiment data. + ''' + + # Create the configuration dictionary. + CONFIG_FACTORY = ConfigFactory() + config = CONFIG_FACTORY.merge() + + # Create an environment + env_func = partial(make, + config.task, + **config.task_config + ) + random_env = env_func(gui=False) + + # Create controller. + ctrl = make(config.algo, + env_func, + **config.algo_config + ) + + all_trajs = defaultdict(list) + n_episodes = 1 if n_episodes is None else n_episodes + + # Run the experiment. + for _ in range(n_episodes): + # Get initial state and create environments + init_state, _ = random_env.reset() + static_env = env_func(gui=gui, randomized_init=False, init_state=init_state) + static_train_env = env_func(gui=False, randomized_init=False, init_state=init_state) + + # Create experiment, train, and run evaluation + experiment = BaseExperiment(env=static_env, ctrl=ctrl, train_env=static_train_env) + experiment.launch_training() + + if n_steps is None: + trajs_data, _ = experiment.run_evaluation(training=True, n_episodes=1) + else: + trajs_data, _ = experiment.run_evaluation(training=True, n_steps=n_steps) + + if gui: + post_analysis(trajs_data['obs'][0], trajs_data['action'][0], ctrl.env) + + # Close environments + static_env.close() + static_train_env.close() + + # Merge in new trajectory data + for key, value in trajs_data.items(): + all_trajs[key] += value + + ctrl.close() + random_env.close() + metrics = experiment.compute_metrics(all_trajs) + all_trajs = dict(all_trajs) + + if save_data: + results = {'trajs_data': all_trajs, 'metrics': metrics} + path_dir = os.path.dirname('./temp-data/') + os.makedirs(path_dir, exist_ok=True) + with open(f'./temp-data/{config.algo}_data_{config.task}_{config.task_config.task}.pkl', 'wb') as file: + pickle.dump(results, file) + + print('FINAL METRICS - ' + ', '.join([f'{key}: {value}' for key, value in metrics.items()])) + + +def post_analysis(state_stack, input_stack, env): + '''Plots the input and states to determine iLQR's success. + + Args: + state_stack (ndarray): The list of observations of iLQR in the latest run. + input_stack (ndarray): The list of inputs of iLQR in the latest run. + ''' + model = env.symbolic + stepsize = model.dt + + plot_length = np.min([np.shape(input_stack)[0], np.shape(state_stack)[0]]) + times = np.linspace(0, stepsize * plot_length, plot_length) + + reference = env.X_GOAL + if env.TASK == Task.STABILIZATION: + reference = np.tile(reference.reshape(1, model.nx), (plot_length, 1)) + + # Plot states + fig, axs = plt.subplots(model.nx) + for k in range(model.nx): + axs[k].plot(times, np.array(state_stack).transpose()[k, 0:plot_length], label='actual') + axs[k].plot(times, reference.transpose()[k, 0:plot_length], color='r', label='desired') + axs[k].set(ylabel=env.STATE_LABELS[k] + f'\n[{env.STATE_UNITS[k]}]') + axs[k].yaxis.set_major_formatter(FormatStrFormatter('%.1f')) + if k != model.nx - 1: + axs[k].set_xticks([]) + axs[0].set_title('State Trajectories') + axs[-1].legend(ncol=3, bbox_transform=fig.transFigure, bbox_to_anchor=(1, 0), loc='lower right') + axs[-1].set(xlabel='time (sec)') + + # Plot inputs + _, axs = plt.subplots(model.nu) + if model.nu == 1: + axs = [axs] + for k in range(model.nu): + axs[k].plot(times, np.array(input_stack).transpose()[k, 0:plot_length]) + axs[k].set(ylabel=f'input {k}') + axs[k].set(ylabel=env.ACTION_LABELS[k] + f'\n[{env.ACTION_UNITS[k]}]') + axs[k].yaxis.set_major_formatter(FormatStrFormatter('%.1f')) + axs[0].set_title('Input Trajectories') + axs[-1].set(xlabel='time (sec)') + + plt.show() + + +def wrap2pi_vec(angle_vec): + '''Wraps a vector of angles between -pi and pi. + + Args: + angle_vec (ndarray): A vector of angles. + ''' + for k, angle in enumerate(angle_vec): + while angle > np.pi: + angle -= np.pi + while angle <= -np.pi: + angle += np.pi + angle_vec[k] = angle + return angle_vec + + +if __name__ == '__main__': + run() diff --git a/examples/lqr/lqr_experiment.sh b/examples/lqr/lqr_experiment.sh new file mode 100755 index 000000000..c8b620fa9 --- /dev/null +++ b/examples/lqr/lqr_experiment.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# LQR Experiment. + +# SYS='cartpole' +# SYS='quadrotor_2D' +SYS='quadrotor_3D' + +# TASK='stabilization' +TASK='tracking' + +ALGO='lqr' +# ALGO='ilqr' + +if [ "$SYS" == 'cartpole' ]; then + SYS_NAME=$SYS +else + SYS_NAME='quadrotor' +fi + +python3 ./lqr_experiment.py \ + --task ${SYS_NAME} \ + --algo ${ALGO} \ + --overrides \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + ./config_overrides/${SYS}/${ALGO}_${SYS}_${TASK}.yaml diff --git a/examples/mpsc/config_overrides/cartpole/cartpole_stab.yaml b/examples/mpsc/config_overrides/cartpole/cartpole_stab.yaml new file mode 100644 index 000000000..3274cbc41 --- /dev/null +++ b/examples/mpsc/config_overrides/cartpole/cartpole_stab.yaml @@ -0,0 +1,72 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 15 + pyb_freq: 750 + physics: pyb + + # state initialization + init_state: + init_x: 0.1 + init_x_dot: -1.5 + init_theta: -0.175 + init_theta_dot: 0.5 + randomized_init: True + randomized_inertial_prop: False + normalized_rl_action_space: True + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + + task: stabilization + task_info: + stabilization_goal: [0.7, 0] + stabilization_goal_tolerance: 0.05 + + inertial_prop: + pole_length: 0.5 + cart_mass: 1 + pole_mass: 0.1 + + episode_len_sec: 10 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 1, 1, 1] + rew_act_weight: 0.1 + rew_exponential: True + + # constraints + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 0.18 + - 2 + lower_bounds: + - -2 + - -2 + - -0.18 + - -2 + - constraint_form: default_constraint + constrained_variable: input + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/mpsc/config_overrides/cartpole/cartpole_track.yaml b/examples/mpsc/config_overrides/cartpole/cartpole_track.yaml new file mode 100644 index 000000000..30b9f3833 --- /dev/null +++ b/examples/mpsc/config_overrides/cartpole/cartpole_track.yaml @@ -0,0 +1,77 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 15 + pyb_freq: 750 + physics: pyb + + # state initialization + init_state: + init_x: 0 + init_x_dot: 0 + init_theta: 0 + init_theta_dot: 0 + randomized_init: True + randomized_inertial_prop: False + normalized_rl_action_space: True + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + + task: traj_tracking + task_info: + stabilization_goal: [0, 0] + stabilization_goal_tolerance: 0.05 + trajectory_type: 'circle' + num_cycles: 2 + trajectory_plane: 'zx' + trajectory_position_offset: [0, 0] + trajectory_scale: 1 + + inertial_prop: + pole_length: 0.5 + cart_mass: 1 + pole_mass: 0.1 + + episode_len_sec: 10 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 0.01, 0.01, 0.01] + rew_act_weight: 0.01 + rew_exponential: True + + # constraints + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 0.18 + - 2 + lower_bounds: + - -2 + - -2 + - -0.18 + - -2 + - constraint_form: default_constraint + constrained_variable: input + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/mpsc/config_overrides/cartpole/linear_mpsc_cartpole.yaml b/examples/mpsc/config_overrides/cartpole/linear_mpsc_cartpole.yaml new file mode 100644 index 000000000..7a538c0ec --- /dev/null +++ b/examples/mpsc/config_overrides/cartpole/linear_mpsc_cartpole.yaml @@ -0,0 +1,33 @@ +safety_filter: linear_mpsc +sf_config: + # LQR controller parameters + r_lin: + - 0.1 + q_lin: + - 1 + - 1 + - 1 + - 1 + + # MPC Parameters + horizon: 20 + warmstart: True + integration_algo: rk4 + use_terminal_set: False + + # Prior info + prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null + + # Safe set calculation + n_samples: 600 + n_samples_terminal_set: 100 + learn_terminal_set: False + + # Tau parameter for the calcuation of the RPI + tau: 0.95 + + # Cost function + cost_function: one_step_cost diff --git a/examples/mpsc/config_overrides/cartpole/lqr_cartpole.yaml b/examples/mpsc/config_overrides/cartpole/lqr_cartpole.yaml new file mode 100644 index 000000000..f3dcfbd44 --- /dev/null +++ b/examples/mpsc/config_overrides/cartpole/lqr_cartpole.yaml @@ -0,0 +1,13 @@ +algo: lqr +algo_config: + # Cost parameters + r_lin: + - 0.1 + q_lin: + - 1 + - 1 + - 1 + - 1 + + # Model arguments + discrete_dynamics: True diff --git a/examples/mpsc/config_overrides/cartpole/ppo_cartpole.yaml b/examples/mpsc/config_overrides/cartpole/ppo_cartpole.yaml new file mode 100644 index 000000000..838c16d5c --- /dev/null +++ b/examples/mpsc/config_overrides/cartpole/ppo_cartpole.yaml @@ -0,0 +1,40 @@ +algo: ppo +algo_config: + # model args + hidden_dim: 64 + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # loss args + gamma: 0.99 + use_gae: True + gae_lambda: 0.95 + use_clipped_value: False + clip_param: 0.2 + target_kl: 0.01 + entropy_coef: 0.01 + + # optim args + opt_epochs: 10 + mini_batch_size: 64 + actor_lr: 0.0003 + critic_lr: 0.001 + max_grad_norm: 0.5 + + # runner args + max_env_steps: 100000 + num_workers: 1 + rollout_batch_size: 4 + rollout_steps: 100 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 1000 + save_interval: 1000 + num_checkpoints: 0 + eval_interval: 1000 + eval_save_best: True + tensorboard: False diff --git a/examples/mpsc/config_overrides/cartpole/sac_cartpole.yaml b/examples/mpsc/config_overrides/cartpole/sac_cartpole.yaml new file mode 100644 index 000000000..fc36a8d61 --- /dev/null +++ b/examples/mpsc/config_overrides/cartpole/sac_cartpole.yaml @@ -0,0 +1,29 @@ +algo: sac +algo_config: + # model args + hidden_dim: 64 + use_entropy_tuning: False + + # optim args + train_interval: 100 + train_batch_size: 64 + actor_lr: 0.001 + critic_lr: 0.001 + entropy_lr: 0.001 + + # runner args + max_env_steps: 100000 + warm_up_steps: 1000 + rollout_batch_size: 4 + num_workers: 1 + max_buffer_size: 1000000 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 1000 + save_interval: 1000 + num_checkpoints: 100 + eval_interval: 1000 + eval_save_best: True + tensorboard: True diff --git a/examples/mpsc/config_overrides/quadrotor_2D/linear_mpsc_quadrotor_2D.yaml b/examples/mpsc/config_overrides/quadrotor_2D/linear_mpsc_quadrotor_2D.yaml new file mode 100644 index 000000000..476468a46 --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_2D/linear_mpsc_quadrotor_2D.yaml @@ -0,0 +1,35 @@ +safety_filter: linear_mpsc +sf_config: + # LQR controller parameters + r_lin: + - 0.1 + q_lin: + - 1 + - 1 + - 1 + - 1 + - 1 + - 1 + + # MPC Parameters + horizon: 20 + warmstart: True + integration_algo: rk4 + use_terminal_set: False + + # Prior info + prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null + + # Safe set calculation + n_samples: 600 + n_samples_terminal_set: 10 + learn_terminal_set: False + + # Tau parameter for the calcuation of the RPI + tau: 0.975 + + # Cost function + cost_function: one_step_cost diff --git a/examples/mpsc/config_overrides/quadrotor_2D/lqr_quadrotor_2D.yaml b/examples/mpsc/config_overrides/quadrotor_2D/lqr_quadrotor_2D.yaml new file mode 100644 index 000000000..3ff048ecf --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_2D/lqr_quadrotor_2D.yaml @@ -0,0 +1,15 @@ +algo: lqr +algo_config: + # Cost parameters + r_lin: + - 0.1 + q_lin: + - 1 + - 1 + - 1 + - 1 + - 1 + - 1 + + # Model arguments + discrete_dynamics: True diff --git a/examples/mpsc/config_overrides/quadrotor_2D/pid_quadrotor_2D.yaml b/examples/mpsc/config_overrides/quadrotor_2D/pid_quadrotor_2D.yaml new file mode 100644 index 000000000..01a303668 --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_2D/pid_quadrotor_2D.yaml @@ -0,0 +1,20 @@ +algo: pid +algo_config: + g: 9.8 + + KF: 3.16e-10 + KM: 7.94e-12 + + P_COEFF_FOR: [.4, .4, 1.25] + I_COEFF_FOR: [.05, .05, .05] + D_COEFF_FOR: [.2, .2, .5] + + P_COEFF_TOR: [70000., 70000., 60000.] + I_COEFF_TOR: [.0, .0, 500.] + D_COEFF_TOR: [20000., 20000., 12000.] + + PWM2RPM_SCALE: 0.2685 + PWM2RPM_CONST: 4070.3 + + MIN_PWM: 20000 + MAX_PWM: 65535 diff --git a/examples/mpsc/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml b/examples/mpsc/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml new file mode 100644 index 000000000..6a1af4acc --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml @@ -0,0 +1,84 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + physics: pyb + quad_type: 2 + + init_state: + init_x: 0.5 + init_x_dot: 0 + init_z: 1.5 + init_z_dot: 0 + init_theta: 0 + init_theta_dot: 0 + randomized_init: True + randomized_inertial_prop: False + normalized_rl_action_space: True + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 1 + high: 2 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + + task: stabilization + task_info: + stabilization_goal: [0, 1] + stabilization_goal_tolerance: 0.1 + + inertial_prop: + M: 0.027 + Iyy: 1.4e-05 + + episode_len_sec: 6 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 1, 1, 1, 1, 1] + rew_act_weight: 0.1 + rew_exponential: True + + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 2 + - 2 + - 0.2 + - 1 + lower_bounds: + - -2 + - -2 + - 0 + - -2 + - -0.2 + - -1 + - constraint_form: default_constraint + constrained_variable: input + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/mpsc/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml b/examples/mpsc/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml new file mode 100644 index 000000000..a65baa314 --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml @@ -0,0 +1,89 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + physics: pyb + quad_type: 2 + + init_state: + init_x: 0 + init_x_dot: 0 + init_z: 1 + init_z_dot: 0 + init_theta: 0 + init_theta_dot: 0 + randomized_init: True + randomized_inertial_prop: False + normalized_rl_action_space: True + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 1 + high: 2 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + + task: traj_tracking + task_info: + stabilization_goal: [0, 1] + stabilization_goal_tolerance: 0.01 + trajectory_type: figure8 + num_cycles: 1 + trajectory_plane: 'xz' + trajectory_position_offset: [0, 1] + trajectory_scale: 1 + + inertial_prop: + M: 0.027 + Iyy: 1.4e-05 + + episode_len_sec: 6 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 0.01, 1, 0.01, 0.01, 0.01] + rew_act_weight: 0.01 + rew_exponential: True + + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 2 + - 2 + - 0.2 + - 1 + lower_bounds: + - -2 + - -2 + - 0 + - -2 + - -0.2 + - -1 + - constraint_form: default_constraint + constrained_variable: input + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/mpsc/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml b/examples/mpsc/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml new file mode 100644 index 000000000..b238bb11b --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml @@ -0,0 +1,29 @@ +algo: sac +algo_config: + # model args + hidden_dim: 256 + use_entropy_tuning: False + + # optim args + train_interval: 100 + train_batch_size: 64 + actor_lr: 0.001 + critic_lr: 0.001 + entropy_lr: 0.001 + + # runner args + max_env_steps: 100000 + warm_up_steps: 1000 + rollout_batch_size: 4 + num_workers: 1 + max_buffer_size: 1000000 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 1000 + save_interval: 1000 + num_checkpoints: 100 + eval_interval: 1000 + eval_save_best: True + tensorboard: True diff --git a/examples/mpsc/config_overrides/quadrotor_3D/linear_mpsc_quadrotor_3D.yaml b/examples/mpsc/config_overrides/quadrotor_3D/linear_mpsc_quadrotor_3D.yaml new file mode 100644 index 000000000..1d0a2b8fa --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_3D/linear_mpsc_quadrotor_3D.yaml @@ -0,0 +1,41 @@ +safety_filter: linear_mpsc +sf_config: + # LQR controller parameters + r_lin: + - 0.1 + q_lin: + - 0.01 + - 0.01 + - 0.01 + - 0.01 + - 0.01 + - 0.01 + - 1 + - 1 + - 1 + - 1 + - 1 + - 1 + + # MPC Parameters + horizon: 20 + warmstart: True + integration_algo: rk4 + use_terminal_set: False + + # Prior info + prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null + + # Safe set calculation + n_samples: 600 + n_samples_terminal_set: 10 + learn_terminal_set: False + + # Tau parameter for the calcuation of the RPI + tau: 0.975 + + # Cost function + cost_function: one_step_cost diff --git a/examples/mpsc/config_overrides/quadrotor_3D/pid_quadrotor_3D.yaml b/examples/mpsc/config_overrides/quadrotor_3D/pid_quadrotor_3D.yaml new file mode 100644 index 000000000..01a303668 --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_3D/pid_quadrotor_3D.yaml @@ -0,0 +1,20 @@ +algo: pid +algo_config: + g: 9.8 + + KF: 3.16e-10 + KM: 7.94e-12 + + P_COEFF_FOR: [.4, .4, 1.25] + I_COEFF_FOR: [.05, .05, .05] + D_COEFF_FOR: [.2, .2, .5] + + P_COEFF_TOR: [70000., 70000., 60000.] + I_COEFF_TOR: [.0, .0, 500.] + D_COEFF_TOR: [20000., 20000., 12000.] + + PWM2RPM_SCALE: 0.2685 + PWM2RPM_CONST: 4070.3 + + MIN_PWM: 20000 + MAX_PWM: 65535 diff --git a/examples/mpsc/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml b/examples/mpsc/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml new file mode 100644 index 000000000..7712d5313 --- /dev/null +++ b/examples/mpsc/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml @@ -0,0 +1,128 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + physics: pyb + quad_type: 3 + + init_state: + init_x: 0.5 + init_x_dot: 0 + init_y: 0.5 + init_y_dot: 0 + init_z: 1.5 + init_z_dot: 0 + init_phi: 0 + init_theta: 0 + init_psi: 0 + init_p: 0 + init_q: 0 + init_r: 0 + randomized_init: True + randomized_inertial_prop: False + normalized_rl_action_space: True + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_y: + distrib: 'uniform' + low: -1 + high: 1 + init_y_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 1 + high: 2 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_q: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_r: + distrib: 'uniform' + low: -0.1 + high: 0.1 + + task: stabilization + task_info: + stabilization_goal: [0, 0, 1] + stabilization_goal_tolerance: 0.1 + + inertial_prop: + M: 0.027 + Ixx: 1.4e-5 + Iyy: 1.4e-5 + Izz: 2.17e-5 + + episode_len_sec: 6 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + rew_act_weight: 0.1 + rew_exponential: True + + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 2 + - 2 + - 2 + - 2 + - 0.2 + - 1 + - 0.2 + - 1 + - 0.2 + - 1 + lower_bounds: + - -2 + - -2 + - -2 + - -2 + - 0 + - -2 + - -0.2 + - -1 + - -0.2 + - -1 + - -0.2 + - -1 + - constraint_form: default_constraint + constrained_variable: input + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/mpsc/models/linear_mpsc_cartpole.pkl b/examples/mpsc/models/linear_mpsc_cartpole.pkl new file mode 100644 index 000000000..16ea929f2 Binary files /dev/null and b/examples/mpsc/models/linear_mpsc_cartpole.pkl differ diff --git a/examples/mpsc/models/linear_mpsc_quadrotor_2D.pkl b/examples/mpsc/models/linear_mpsc_quadrotor_2D.pkl new file mode 100644 index 000000000..bebdc07ab Binary files /dev/null and b/examples/mpsc/models/linear_mpsc_quadrotor_2D.pkl differ diff --git a/examples/mpsc/models/ppo_model_cartpole_stab.pt b/examples/mpsc/models/ppo_model_cartpole_stab.pt new file mode 100644 index 000000000..6a1e69978 Binary files /dev/null and b/examples/mpsc/models/ppo_model_cartpole_stab.pt differ diff --git a/examples/mpsc/models/ppo_model_cartpole_track.pt b/examples/mpsc/models/ppo_model_cartpole_track.pt new 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new file mode 100644 index 000000000..c5628cf21 Binary files /dev/null and b/examples/mpsc/models/sac_model_cartpole_track.pt differ diff --git a/examples/mpsc/models/sac_model_quadrotor_2D_stab.pt b/examples/mpsc/models/sac_model_quadrotor_2D_stab.pt new file mode 100644 index 000000000..c06d6ad13 Binary files /dev/null and b/examples/mpsc/models/sac_model_quadrotor_2D_stab.pt differ diff --git a/examples/mpsc/models/sac_model_quadrotor_2D_track.pt b/examples/mpsc/models/sac_model_quadrotor_2D_track.pt new file mode 100644 index 000000000..7375c4f50 Binary files /dev/null and b/examples/mpsc/models/sac_model_quadrotor_2D_track.pt differ diff --git a/examples/mpsc/mpsc_experiment.py b/examples/mpsc/mpsc_experiment.py new file mode 100644 index 000000000..151070fc7 --- /dev/null +++ b/examples/mpsc/mpsc_experiment.py @@ -0,0 +1,204 @@ +'''This script tests the MPSC safety filter implementation.''' + +import os +import shutil +from functools import partial + +import matplotlib.pyplot as plt +import numpy as np + +from safe_control_gym.envs.benchmark_env import Cost, Environment, Task +from safe_control_gym.experiments.base_experiment import BaseExperiment +from safe_control_gym.utils.configuration import ConfigFactory +from safe_control_gym.utils.registration import make + + +def run(plot=True, training=False, n_episodes=1, n_steps=None, curr_path='.'): + '''Main function to run MPSC experiments. + + Args: + plot (bool): Whether to plot the results. + training (bool): Whether to train the MPSC or load pre-trained values. + n_episodes (int): The number of episodes to execute. + n_steps (int): How many steps to run the experiment. + curr_path (str): The current relative path to the experiment folder. + ''' + + # Create the configuration dictionary. + fac = ConfigFactory() + config = fac.merge() + config.task_config['randomized_init'] = False + if config.algo in ['ppo', 'sac']: + config.task_config['cost'] = Cost.RL_REWARD + config.algo_config['training'] = False + else: + config.task_config['cost'] = Cost.QUADRATIC + config.task_config['normalized_rl_action_space'] = False + + if config.task == Environment.QUADROTOR: + system = f'quadrotor_{str(config.task_config.quad_type)}D' + else: + system = config.task + + # Create an environment + env_func = partial(make, + config.task, + **config.task_config) + env = env_func() + + # Setup controller. + ctrl = make(config.algo, + env_func, + **config.algo_config, + output_dir=curr_path + '/temp') + + if config.algo in ['ppo', 'sac']: + # Load state_dict from trained. + model_dir = os.path.dirname(os.path.abspath(__file__)) + '/models' + task = 'stab' if config.task_config.task == Task.STABILIZATION else 'track' + ctrl.load(os.path.join(model_dir, f'{config.algo}_model_{system}_{task}.pt')) + + # Remove temporary files and directories + shutil.rmtree(os.path.dirname(os.path.abspath(__file__)) + '/temp', ignore_errors=True) + + # Run without safety filter + experiment = BaseExperiment(env, ctrl) + results, uncert_metrics = experiment.run_evaluation(n_episodes=n_episodes, n_steps=n_steps) + elapsed_time_uncert = results['timestamp'][0][-1] - results['timestamp'][0][0] + + # Setup MPSC. + config.task_config['normalized_rl_action_space'] = False + env_func_filter = partial(make, + config.task, + **config.task_config) + safety_filter = make(config.safety_filter, + env_func_filter, + **config.sf_config) + safety_filter.reset() + + if training is True: + train_env = env_func(randomized_init=True, + init_state=None, + cost='quadratic', + normalized_rl_action_space=False, + disturbance=None, + ) + safety_filter.learn(env=train_env) + safety_filter.save(path=f'{curr_path}/models/{config.safety_filter}_{system}.pkl') + else: + safety_filter.load(path=f'{curr_path}/models/{config.safety_filter}_{system}.pkl') + + ctrl.reset() + + # Run with safety filter + experiment = BaseExperiment(env, ctrl, safety_filter=safety_filter) + certified_results, cert_metrics = experiment.run_evaluation(n_episodes=n_episodes, n_steps=n_steps) + ctrl.close() + mpsc_results = certified_results['safety_filter_data'][0] + safety_filter.close() + + elapsed_time_cert = results['timestamp'][0][-1] - results['timestamp'][0][0] + + corrections = mpsc_results['correction'][0] > 1e-6 + corrections = np.append(corrections, False) + + if plot is True: + if config.task == Environment.CARTPOLE: + graph1_1 = 2 + graph1_2 = 3 + graph3_1 = 0 + graph3_2 = 1 + elif config.task == Environment.QUADROTOR: + graph1_1 = 4 + graph1_2 = 5 + graph3_1 = 0 + graph3_2 = 2 + + _, ax = plt.subplots() + ax.plot(results['obs'][0][:, graph1_1], results['obs'][0][:, graph1_2], 'r--', label='Uncertified') + ax.plot(certified_results['obs'][0][:, graph1_1], certified_results['obs'][0][:, graph1_2], '.-', label='Certified') + ax.plot(certified_results['obs'][0][corrections, graph1_1], certified_results['obs'][0][corrections, graph1_2], 'r.', label='Modified') + ax.scatter(results['obs'][0][0, graph1_1], results['obs'][0][0, graph1_2], color='g', marker='o', s=100, label='Initial State') + if config.task == Environment.CARTPOLE: + theta_constraint = config.task_config['constraints'][0].upper_bounds[2] + elif config.task == Environment.QUADROTOR: + theta_constraint = config.task_config['constraints'][0].upper_bounds[4] + ax.axvline(x=-theta_constraint, color='k', lw=2, label='Limit') + ax.axvline(x=theta_constraint, color='k', lw=2) + ax.set_xlabel(r'$\theta$') + ax.set_ylabel(r'$\dot{\theta}$') + ax.set_box_aspect(0.5) + ax.legend(loc='upper right') + + if config.task_config.task == Task.TRAJ_TRACKING and config.task == Environment.CARTPOLE: + _, ax2 = plt.subplots() + ax2.plot(np.linspace(0, 20, certified_results['obs'][0].shape[0]), safety_filter.env.X_GOAL[:, 0], 'g--', label='Reference') + ax2.plot(np.linspace(0, 20, results['obs'][0].shape[0]), results['obs'][0][:, 0], 'r--', label='Uncertified') + ax2.plot(np.linspace(0, 20, certified_results['obs'][0].shape[0]), certified_results['obs'][0][:, 0], '.-', label='Certified') + ax2.plot(np.linspace(0, 20, certified_results['obs'][0].shape[0])[corrections], certified_results['obs'][0][corrections, 0], 'r.', label='Modified') + ax2.set_xlabel(r'Time') + ax2.set_ylabel(r'X') + ax2.set_box_aspect(0.5) + ax2.legend(loc='upper right') + elif config.task == Environment.QUADROTOR: + _, ax2 = plt.subplots() + ax2.plot(results['obs'][0][:, 1], results['obs'][0][:, 3], 'r--', label='Uncertified') + ax2.plot(certified_results['obs'][0][:, 1], certified_results['obs'][0][:, 3], '.-', label='Certified') + ax2.plot(certified_results['obs'][0][corrections, 1], certified_results['obs'][0][corrections, 3], 'r.', label='Modified') + ax2.set_xlabel(r'x_dot') + ax2.set_ylabel(r'z_dot') + ax2.set_box_aspect(0.5) + ax2.legend(loc='upper right') + + _, ax3 = plt.subplots() + ax3.plot(results['obs'][0][:, graph3_1], results['obs'][0][:, graph3_2], 'r--', label='Uncertified') + ax3.plot(certified_results['obs'][0][:, graph3_1], certified_results['obs'][0][:, graph3_2], '.-', label='Certified') + if config.task_config.task == Task.TRAJ_TRACKING and config.task == Environment.QUADROTOR: + ax3.plot(safety_filter.env.X_GOAL[:, 0], safety_filter.env.X_GOAL[:, 2], 'g--', label='Reference') + ax3.plot(certified_results['obs'][0][corrections, graph3_1], certified_results['obs'][0][corrections, graph3_2], 'r.', label='Modified') + ax3.scatter(results['obs'][0][0, graph3_1], results['obs'][0][0, graph3_2], color='g', marker='o', s=100, label='Initial State') + ax3.set_xlabel(r'X') + if config.task == Environment.CARTPOLE: + ax3.set_ylabel(r'Vel') + elif config.task == Environment.QUADROTOR: + ax3.set_ylabel(r'Z') + ax3.set_box_aspect(0.5) + ax3.legend(loc='upper right') + + _, ax_act = plt.subplots() + if config.task == Environment.CARTPOLE: + ax_act.plot(certified_results['current_physical_action'][0][:], 'b-', label='Certified Input') + ax_act.plot(mpsc_results['uncertified_action'][0][:], 'r--', label='Attempted Input') + ax_act.plot(results['current_physical_action'][0][:], 'g--', label='Uncertified Input') + else: + ax_act.plot(certified_results['current_physical_action'][0][:, 0], 'b-', label='Certified Input 1') + ax_act.plot(certified_results['current_physical_action'][0][:, 1], 'b--', label='Certified Input 2') + ax_act.plot(mpsc_results['uncertified_action'][0][:, 0], 'r-', label='Attempted Input 1') + ax_act.plot(mpsc_results['uncertified_action'][0][:, 1], 'r--', label='Attempted Input 2') + ax_act.plot(results['current_physical_action'][0][:, 0], 'g-', label='Uncertified Input 1') + ax_act.plot(results['current_physical_action'][0][:, 1], 'g--', label='Uncertified Input 2') + ax_act.legend() + ax_act.set_title('Input comparison') + ax_act.set_xlabel('Step') + ax_act.set_ylabel('Input') + ax_act.set_box_aspect(0.5) + + print(f'Total Uncertified Time: {elapsed_time_uncert}s') + print(f'Total Certified Time: {elapsed_time_cert}s') + print('Number of Corrections: ', np.sum(corrections)) + print('Sum of Corrections: ', np.linalg.norm(mpsc_results['correction'][0])) + print('Max Correction: ', np.max(np.abs(mpsc_results['correction'][0]))) + print('Number of Feasible Iterations: ', np.sum(mpsc_results['feasible'][0])) + print('Total Number of Iterations: ', uncert_metrics['average_length']) + print('Total Number of Certified Iterations: ', cert_metrics['average_length']) + print('Number of Violations: ', uncert_metrics['average_constraint_violation']) + print('Number of Certified Violations: ', cert_metrics['average_constraint_violation']) + print('RMSE Uncertified: ', uncert_metrics['average_rmse']) + print('RMSE Certified: ', cert_metrics['average_rmse']) + + plt.tight_layout() + plt.show() + + +if __name__ == '__main__': + run() diff --git a/examples/mpsc/mpsc_experiment.sh b/examples/mpsc/mpsc_experiment.sh new file mode 100755 index 000000000..c121d5d16 --- /dev/null +++ b/examples/mpsc/mpsc_experiment.sh @@ -0,0 +1,34 @@ +#!/bin/bash + +SYS='cartpole' +# SYS='quadrotor_2D' + +# TASK='stab' +TASK='track' + +# ALGO='lqr' +# ALGO='pid' +ALGO='ppo' +# ALGO='sac' + +SAFETY_FILTER='linear_mpsc' + +MPSC_COST='one_step_cost' + +if [ "$SYS" == 'cartpole' ]; then + SYS_NAME=$SYS +else + SYS_NAME='quadrotor' +fi + +# Model-predictive safety certification of an unsafe controller. +python3 ./mpsc_experiment.py \ + --task ${SYS_NAME} \ + --algo ${ALGO} \ + --safety_filter ${SAFETY_FILTER} \ + --overrides \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + ./config_overrides/${SYS}/${ALGO}_${SYS}.yaml \ + ./config_overrides/${SYS}/${SAFETY_FILTER}_${SYS}.yaml \ + --kv_overrides \ + sf_config.cost_function=${MPSC_COST} diff --git a/examples/mpsc/train_rl_model.sh b/examples/mpsc/train_rl_model.sh new file mode 100755 index 000000000..f1ac98b6a --- /dev/null +++ b/examples/mpsc/train_rl_model.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +SYS='cartpole' +# SYS='quadrotor' + +TASK='stab' +# TASK='track' + +ALGO='ppo' +# ALGO='sac' + +# Removed the temporary data used to train the new unsafe model. +rm -r -f ./unsafe_rl_temp_data/ + +# Train the unsafe controller/agent. +python3 ../../safe_control_gym/experiments/train_rl_controller.py \ + --algo ${ALGO} \ + --task ${SYS} \ + --overrides \ + ./config_overrides/${SYS}/${ALGO}_${SYS}.yaml \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + --output_dir ./ \ + --tag unsafe_rl_temp_data/ \ + --seed 2 + +# Move the newly trained unsafe model. +mv ./unsafe_rl_temp_data/seed2_*/model_best.pt ./models/${ALGO}_model_${SYS}_${TASK}.pt + +# Removed the temporary data used to train the new unsafe model. +rm -r -f ./unsafe_rl_temp_data/ diff --git a/examples/pid/config_overrides/pid.yaml b/examples/pid/config_overrides/pid.yaml new file mode 100644 index 000000000..279e0a064 --- /dev/null +++ b/examples/pid/config_overrides/pid.yaml @@ -0,0 +1,7 @@ +algo_config: + # this is the default params in 2D quadrotor so don't need to specify again here + # but it serves as a example/template if any change to the prior is needed + prior_info: + prior_prop: + M: 0.027 + Iyy: 0.000014 diff --git a/examples/pid/config_overrides/quadrotor_2D/quadrotor_2D_stabilization.yaml b/examples/pid/config_overrides/quadrotor_2D/quadrotor_2D_stabilization.yaml new file mode 100644 index 000000000..6179a6deb --- /dev/null +++ b/examples/pid/config_overrides/quadrotor_2D/quadrotor_2D_stabilization.yaml @@ -0,0 +1,45 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 2 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.5 + high: 1.5 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: stabilization + task_info: + stabilization_goal: [0, 1] + stabilization_goal_tolerance: 0.0 + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/pid/config_overrides/quadrotor_2D/quadrotor_2D_tracking.yaml b/examples/pid/config_overrides/quadrotor_2D/quadrotor_2D_tracking.yaml new file mode 100644 index 000000000..aa68b6912 --- /dev/null +++ b/examples/pid/config_overrides/quadrotor_2D/quadrotor_2D_tracking.yaml @@ -0,0 +1,48 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 2 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.5 + high: 1.5 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: traj_tracking + task_info: + trajectory_type: figure8 + num_cycles: 1 + trajectory_plane: 'xz' + trajectory_position_offset: [0, 1] + trajectory_scale: 0.75 + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/pid/config_overrides/quadrotor_3D/quadrotor_3D_stabilization.yaml b/examples/pid/config_overrides/quadrotor_3D/quadrotor_3D_stabilization.yaml new file mode 100644 index 000000000..8ebffccb7 --- /dev/null +++ b/examples/pid/config_overrides/quadrotor_3D/quadrotor_3D_stabilization.yaml @@ -0,0 +1,71 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 3 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -1 + high: 1 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_y: + distrib: 'uniform' + low: -1 + high: 1 + init_y_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.5 + high: 1.5 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_q: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_r: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: stabilization + task_info: + stabilization_goal: [0, 0, 1] + stabilization_goal_tolerance: 0.0 + proj_point: [0, 0, 0.5] + proj_normal: [0, 1, 1] + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/pid/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml b/examples/pid/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml new file mode 100644 index 000000000..17cfa35f5 --- /dev/null +++ b/examples/pid/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml @@ -0,0 +1,74 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + gui: False + physics: pyb + quad_type: 3 + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -0.5 + high: 0.5 + init_x_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_y: + distrib: 'uniform' + low: -0.5 + high: 0.5 + init_y_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_z: + distrib: 'uniform' + low: 0.75 + high: 1.25 + init_z_dot: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_q: + distrib: 'uniform' + low: -0.1 + high: 0.1 + init_r: + distrib: 'uniform' + low: -0.1 + high: 0.1 + randomized_init: True + randomized_inertial_prop: False + + task: traj_tracking + task_info: + trajectory_type: figure8 + num_cycles: 1 + trajectory_plane: 'xz' + trajectory_position_offset: [0, 1] + trajectory_scale: 0.75 + proj_point: [0, 0, 0.5] + proj_normal: [0, 1, 1] + + episode_len_sec: 6 + cost: quadratic + done_on_out_of_bound: True diff --git a/examples/pid/pid_experiment.py b/examples/pid/pid_experiment.py new file mode 100644 index 000000000..958e07efc --- /dev/null +++ b/examples/pid/pid_experiment.py @@ -0,0 +1,132 @@ +'''A PID example on a quadrotor.''' + +import os +import pickle +from functools import partial + +import matplotlib.pyplot as plt +import numpy as np +import pybullet as p + +from safe_control_gym.experiments.base_experiment import BaseExperiment +from safe_control_gym.utils.configuration import ConfigFactory +from safe_control_gym.utils.registration import make + + +def run(gui=True, n_episodes=1, n_steps=None, save_data=False): + '''The main function running PID experiments. + + Args: + gui (bool): Whether to display the gui and plot graphs. + n_episodes (int): The number of episodes to execute. + n_steps (int): The total number of steps to execute. + save_data (bool): Whether to save the collected experiment data. + ''' + + # Create the configuration dictionary. + CONFIG_FACTORY = ConfigFactory() + config = CONFIG_FACTORY.merge() + + config.task_config['gui'] = gui + + custom_trajectory = False + if config.task_config.task == 'traj_tracking' and config.task_config.task_info.trajectory_type == 'custom': + custom_trajectory = True + config.task_config.task_info.trajectory_type = 'circle' # Placeholder + config.task_config.randomized_init = False + config.task_config.init_state = np.zeros((12, 1)) + + # Create an environment + env_func = partial(make, + config.task, + **config.task_config + ) + + # Create controller. + ctrl = make(config.algo, + env_func, + ) + + if custom_trajectory: + # Set iterations and episode counter. + ITERATIONS = int(config.task_config['episode_len_sec'] * config.task_config['ctrl_freq']) + 2 # +2 for start and end of reference + + # Curve fitting with waypoints. + waypoints = np.array([(0, 0, 0), (0.2, 0.5, 0.5), (0.5, 0.1, 0.6), (1, 1, 1), (1.3, 1, 1.2)]) + deg = 6 + t = np.arange(waypoints.shape[0]) + fit_x = np.polyfit(t, waypoints[:, 0], deg) + fit_y = np.polyfit(t, waypoints[:, 1], deg) + fit_z = np.polyfit(t, waypoints[:, 2], deg) + fx = np.poly1d(fit_x) + fy = np.poly1d(fit_y) + fz = np.poly1d(fit_z) + t_scaled = np.linspace(t[0], t[-1], ITERATIONS) + ref_x = fx(t_scaled) + ref_y = fy(t_scaled) + ref_z = fz(t_scaled) + + X_GOAL = np.zeros((ITERATIONS, ctrl.env.symbolic.nx)) + X_GOAL[:, 0] = ref_x + X_GOAL[:, 2] = ref_y + X_GOAL[:, 4] = ref_z + + ctrl.env.X_GOAL = X_GOAL + ctrl.reference = X_GOAL + + obs, _ = ctrl.env.reset() + + if config.task_config.task == 'traj_tracking' and gui is True: + if config.task_config.quad_type == 2: + ref_3D = np.hstack([ctrl.env.X_GOAL[:, [0]], np.zeros(ctrl.env.X_GOAL[:, [0]].shape), ctrl.env.X_GOAL[:, [2]]]) + else: + ref_3D = ctrl.env.X_GOAL[:, [0, 2, 4]] + # Plot in 3D. + ax = plt.axes(projection='3d') + ax.plot3D(ref_3D[:, 0], ref_3D[:, 1], ref_3D[:, 2]) + if custom_trajectory: + ax.scatter3D(waypoints[:, 0], waypoints[:, 1], waypoints[:, 2]) + plt.show() + + for i in range(10, ctrl.env.X_GOAL.shape[0], 10): + p.addUserDebugLine(lineFromXYZ=[ref_3D[i - 10, 0], ref_3D[i - 10, 1], ref_3D[i - 10, 2]], + lineToXYZ=[ref_3D[i, 0], ref_3D[i, 1], ref_3D[i, 2]], + lineColorRGB=[1, 0, 0], + physicsClientId=ctrl.env.PYB_CLIENT) + + if custom_trajectory: + for point in waypoints: + p.loadURDF(os.path.join(ctrl.env.URDF_DIR, 'gate.urdf'), + [point[0], point[1], point[2] - 0.05], + p.getQuaternionFromEuler([0, 0, 0]), + physicsClientId=ctrl.env.PYB_CLIENT) + + # Run the experiment. + experiment = BaseExperiment(ctrl.env, ctrl) + trajs_data, metrics = experiment.run_evaluation(n_episodes=n_episodes, n_steps=n_steps) + experiment.close() + + if save_data: + results = {'trajs_data': trajs_data, 'metrics': metrics} + path_dir = os.path.dirname('./temp-data/') + os.makedirs(path_dir, exist_ok=True) + with open(f'./temp-data/{config.algo}_data_{config.task_config.task}.pkl', 'wb') as file: + pickle.dump(results, file) + + iterations = len(trajs_data['action'][0]) + for i in range(iterations): + # Step the environment and print all returned information. + obs, reward, done, info, action = trajs_data['obs'][0][i], trajs_data['reward'][0][i], trajs_data['done'][0][i], trajs_data['info'][0][i], trajs_data['action'][0][i] + + # Print the last action and the information returned at each step. + print(i, '-th step.') + print(action, '\n', obs, '\n', reward, '\n', done, '\n', info, '\n') + + elapsed_sec = trajs_data['timestamp'][0][-1] - trajs_data['timestamp'][0][0] + print(f'\n{iterations} iterations (@{config.task_config.ctrl_freq}Hz) in {elapsed_sec:.2f} seconds, i.e. {iterations / elapsed_sec:.2f} steps/sec for a {(iterations * (1. / config.task_config.ctrl_freq)) / elapsed_sec:.2f}x speedup.\n') + + print('FINAL METRICS - ' + ', '.join([f'{key}: {value}' for key, value in metrics.items()])) + + +if __name__ == '__main__': + run() diff --git a/examples/pid/pid_experiment.sh b/examples/pid/pid_experiment.sh new file mode 100755 index 000000000..79ab89112 --- /dev/null +++ b/examples/pid/pid_experiment.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +# PID Experiment. + +# SYS='quadrotor_2D' +SYS='quadrotor_3D' + +# TASK='stabilization' +TASK='tracking' + +# TRAJ_TYPE='figure8' +# TRAJ_TYPE='circle' +# TRAJ_TYPE='square' +TRAJ_TYPE='custom' + +python3 ./pid_experiment.py \ + --task quadrotor \ + --algo pid \ + --overrides \ + ./config_overrides/pid.yaml \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + --kv_overrides \ + task_config.task_info.trajectory_type=${TRAJ_TYPE} diff --git a/examples/rl/config_overrides/cartpole/cartpole_stab.yaml b/examples/rl/config_overrides/cartpole/cartpole_stab.yaml new file mode 100644 index 000000000..1f3a0e430 --- /dev/null +++ b/examples/rl/config_overrides/cartpole/cartpole_stab.yaml @@ -0,0 +1,72 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 15 + pyb_freq: 750 + physics: pyb + normalized_rl_action_space: True + + # state initialization + init_state: + init_x: 0.1 + init_x_dot: -1.5 + init_theta: -0.155 + init_theta_dot: 0.75 + randomized_init: True + randomized_inertial_prop: False + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -2 + high: 2 + init_x_dot: + distrib: 'uniform' + low: -2 + high: 2 + init_theta: + distrib: 'uniform' + low: -0.16 + high: 0.16 + init_theta_dot: + distrib: 'uniform' + low: -1 + high: 1 + + task: stabilization + task_info: + stabilization_goal: [0.7, 0] + stabilization_goal_tolerance: 0.0 + + inertial_prop: + pole_length: 0.5 + cart_mass: 1 + pole_mass: 0.1 + + episode_len_sec: 10 + cost: rl_reward + obs_goal_horizon: 0 + + # RL Reward + rew_state_weight: [1, 1, 1, 1] + rew_act_weight: 0.1 + rew_exponential: True + + # constraints + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 0.16 + - 1 + lower_bounds: + - -2 + - -2 + - -0.16 + - -1 + - constraint_form: default_constraint + constrained_variable: input + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/rl/config_overrides/cartpole/cartpole_track.yaml b/examples/rl/config_overrides/cartpole/cartpole_track.yaml new file mode 100644 index 000000000..c889b2c6a --- /dev/null +++ b/examples/rl/config_overrides/cartpole/cartpole_track.yaml @@ -0,0 +1,75 @@ +task_config: + seed: 42 + info_in_reset: True + ctrl_freq: 15 + pyb_freq: 750 + physics: pyb + normalized_rl_action_space: True + + # state initialization + init_state: + init_x: 0 + init_x_dot: 0 + init_theta: 0 + init_theta_dot: 0 + randomized_init: True + randomized_inertial_prop: False + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -2 + high: 2 + init_x_dot: + distrib: 'uniform' + low: -2 + high: 2 + init_theta: + distrib: 'uniform' + low: -0.16 + high: 0.16 + init_theta_dot: + distrib: 'uniform' + low: -1 + high: 1 + + task: traj_tracking + task_info: + trajectory_type: 'circle' + num_cycles: 2 + trajectory_plane: 'zx' + trajectory_position_offset: [0, 0] + trajectory_scale: 1 + + inertial_prop: + pole_length: 0.5 + cart_mass: 1 + pole_mass: 0.1 + + episode_len_sec: 10 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 0.01, 0.01, 0.01] + rew_act_weight: 0.01 + rew_exponential: True + + # constraints + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 2 + - 0.16 + - 1 + lower_bounds: + - -2 + - -2 + - -0.16 + - -1 + - constraint_form: default_constraint + constrained_variable: input + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/rl/config_overrides/cartpole/ppo_cartpole.yaml b/examples/rl/config_overrides/cartpole/ppo_cartpole.yaml new file mode 100644 index 000000000..0dc45648b --- /dev/null +++ b/examples/rl/config_overrides/cartpole/ppo_cartpole.yaml @@ -0,0 +1,41 @@ +algo: ppo +algo_config: + # model args + hidden_dim: 32 + activation: 'leaky_relu' + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # loss args + gamma: 0.98 + use_gae: False + gae_lambda: 0.8 + use_clipped_value: False + clip_param: 0.1 + target_kl: 1.587713889686473e-07 + entropy_coef: 0.00010753631441212628 + + # optim args + opt_epochs: 5 + mini_batch_size: 128 + actor_lr: 0.0007948148615930024 + critic_lr: 0.007497368468753617 + max_grad_norm: 0.5 + + # runner args + max_env_steps: 300000 + num_workers: 1 + rollout_batch_size: 4 + rollout_steps: 150 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 6000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 6000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/cartpole/sac_cartpole.yaml b/examples/rl/config_overrides/cartpole/sac_cartpole.yaml new file mode 100644 index 000000000..0e6b4a406 --- /dev/null +++ b/examples/rl/config_overrides/cartpole/sac_cartpole.yaml @@ -0,0 +1,36 @@ +algo: sac +algo_config: + # model args + hidden_dim: 256 + activation: 'relu' + + # loss args + gamma: 0.98 + tau: 0.12145208815621376 + init_temperature: 0.2 + use_entropy_tuning: False + target_entropy: null + + # optim args + train_interval: 100 + train_batch_size: 512 + actor_lr: 0.00045196308120485273 + critic_lr: 0.022547326782152065 + entropy_lr: 0.001 + + # runner args + max_env_steps: 150000 + warm_up_steps: 100 + rollout_batch_size: 4 + num_workers: 1 + max_buffer_size: 1000000 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 3000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 3000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole.yaml b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole.yaml new file mode 100644 index 000000000..1bbd445fa --- /dev/null +++ b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole.yaml @@ -0,0 +1,47 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 32 + activation: 'leaky_relu' + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # Safety layer args + pretraining: False + pretrained: null + constraint_slack: [0.01, 0.01, 0.01, 0.05, 0.01, 0.01, 0.01, 0.05] + constraint_hidden_dim: 100 + + # loss args + gamma: 0.98 + use_gae: False + gae_lambda: 0.8 + use_clipped_value: False + clip_param: 0.1 + target_kl: 1.587713889686473e-07 + entropy_coef: 0.00010753631441212628 + + # optim args + opt_epochs: 5 + mini_batch_size: 128 + actor_lr: 0.0007948148615930024 + critic_lr: 0.007497368468753617 + max_grad_norm: 0.5 + + # runner args + max_env_steps: 300000 + num_workers: 1 + rollout_batch_size: 4 + rollout_steps: 150 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 6000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 6000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole_pretrain.yaml b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole_pretrain.yaml new file mode 100644 index 000000000..1f11d6022 --- /dev/null +++ b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole_pretrain.yaml @@ -0,0 +1,54 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 32 + activation: 'leaky_relu' + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # Safety layer args + pretraining: True + pretrained: null + constraint_hidden_dim: 100 + constraint_lr: 0.0001 + constraint_batch_size: 256 + constraint_steps_per_epoch: 3000 + constraint_epochs: 200 + constraint_eval_steps: 1500 + constraint_eval_interval: 5 + constraint_buffer_size: 1000000 + constraint_slack: [0.01, 0.01, 0.01, 0.05, 0.01, 0.01, 0.01, 0.05] + + # loss args + gamma: 0.98 + use_gae: False + gae_lambda: 0.8 + use_clipped_value: False + clip_param: 0.1 + target_kl: 1.587713889686473e-07 + entropy_coef: 0.00010753631441212628 + + # optim args + opt_epochs: 5 + mini_batch_size: 128 + actor_lr: 0.0007948148615930024 + critic_lr: 0.007497368468753617 + max_grad_norm: 0.5 + + # runner args + max_env_steps: 100000 + num_workers: 1 + rollout_batch_size: 4 + rollout_steps: 150 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 100 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 100 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml b/examples/rl/config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml new file mode 100644 index 000000000..de1a4a42f --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml @@ -0,0 +1,27 @@ +algo: ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: True + entropy_coef: 0.01 + + # optim args + opt_epochs: 20 + mini_batch_size: 256 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 500000 + rollout_batch_size: 4 + rollout_steps: 1000 + + # misc + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml new file mode 100644 index 000000000..a30ded73b --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml @@ -0,0 +1,90 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + physics: pyb + quad_type: 2 + normalized_rl_action_space: True + + init_state: + init_x: 0.5 + init_x_dot: 0 + init_z: 1.5 + init_z_dot: 0 + init_theta: 0 + init_theta_dot: 0 + randomized_init: True + randomized_inertial_prop: False + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -2 + high: 2 + init_x_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_z: + distrib: 'uniform' + low: 0.3 # Just so it doesn't crash into the ground + high: 2 + init_z_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -1.5 + high: 1.5 + + task: stabilization + task_info: + stabilization_goal: [0, 1] + stabilization_goal_tolerance: 0.0 + + inertial_prop: + M: 0.027 + Iyy: 1.4e-05 + + episode_len_sec: 5 + cost: rl_reward + obs_goal_horizon: 0 + + # RL Reward + rew_state_weight: [1, 1, 1, 1, 1, 1] + rew_act_weight: 0.1 + rew_exponential: True + + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 1 + - 2 + - 1 + - 0.2 + - 1.5 + lower_bounds: + - -2 + - -1 + - 0 + - -1 + - -0.2 + - -1.5 + - constraint_form: default_constraint + constrained_variable: input + upper_bounds: + - 0.29 + - 0.29 + lower_bounds: + - 0.06 + - 0.06 + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml new file mode 100644 index 000000000..2aa366fb5 --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml @@ -0,0 +1,93 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + physics: pyb + quad_type: 2 + normalized_rl_action_space: True + + init_state: + init_x: 0 + init_x_dot: 0 + init_z: 1 + init_z_dot: 0 + init_theta: 0 + init_theta_dot: 0 + randomized_init: True + randomized_inertial_prop: False + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -2 + high: 2 + init_x_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_z: + distrib: 'uniform' + low: 0.3 # Just so it doesn't crash into the ground + high: 2 + init_z_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta_dot: + distrib: 'uniform' + low: -1.5 + high: 1.5 + + task: traj_tracking + task_info: + trajectory_type: figure8 + num_cycles: 1 + trajectory_plane: 'xz' + trajectory_position_offset: [0, 1] + trajectory_scale: 1 + + inertial_prop: + M: 0.027 + Iyy: 1.4e-05 + + episode_len_sec: 5 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 0.01, 1, 0.01, 0.01, 0.01] + rew_act_weight: 0.01 + rew_exponential: True + + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 1 + - 2 + - 1 + - 0.2 + - 1.5 + lower_bounds: + - -2 + - -1 + - 0 + - -1 + - -0.2 + - -1.5 + - constraint_form: default_constraint + constrained_variable: input + upper_bounds: + - 0.29 + - 0.29 + lower_bounds: + - 0.06 + - 0.06 + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/rl/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml b/examples/rl/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml new file mode 100644 index 000000000..74e58b4a6 --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml @@ -0,0 +1,29 @@ +algo: sac +algo_config: + # model args + hidden_dim: 128 + use_entropy_tuning: False + + # optim args + train_interval: 100 + train_batch_size: 256 + actor_lr: 0.001 + critic_lr: 0.001 + entropy_lr: 0.001 + + # runner args + max_env_steps: 200000 + warm_up_steps: 1000 + rollout_batch_size: 4 + num_workers: 1 + max_buffer_size: 1000000 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 4000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 4000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D.yaml b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D.yaml new file mode 100644 index 000000000..20c0afdde --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D.yaml @@ -0,0 +1,33 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: True + entropy_coef: 0.01 + + # Safety layer args + pretraining: False + pretrained: null + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01] + constraint_hidden_dim: 150 + + # optim args + opt_epochs: 20 + mini_batch_size: 250 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 500000 + rollout_batch_size: 4 + rollout_steps: 250 + + # misc + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D_pretrain.yaml b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D_pretrain.yaml new file mode 100644 index 000000000..cf6be1c48 --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D_pretrain.yaml @@ -0,0 +1,40 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: True + entropy_coef: 0.01 + + # Safety layer args + pretraining: True + pretrained: null + constraint_hidden_dim: 150 + constraint_lr: 0.0001 + constraint_batch_size: 256 + constraint_steps_per_epoch: 3000 + constraint_epochs: 500 + constraint_eval_steps: 1500 + constraint_eval_interval: 5 + constraint_buffer_size: 1000000 + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01] + + # optim args + opt_epochs: 20 + mini_batch_size: 250 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 500000 + rollout_batch_size: 4 + rollout_steps: 250 + + # misc + log_interval: 100 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 100 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/ppo_quadrotor_3D.yaml b/examples/rl/config_overrides/quadrotor_3D/ppo_quadrotor_3D.yaml new file mode 100644 index 000000000..e94a7da1c --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/ppo_quadrotor_3D.yaml @@ -0,0 +1,27 @@ +algo: ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: True + entropy_coef: 0.01 + + # optim args + opt_epochs: 20 + mini_batch_size: 256 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 1000000 + rollout_batch_size: 4 + rollout_steps: 1000 + + # misc + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml new file mode 100644 index 000000000..79736724c --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml @@ -0,0 +1,139 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + physics: pyb + quad_type: 3 + normalized_rl_action_space: True + + init_state: + init_x: 0.4 + init_x_dot: 0 + init_y: 0.4 + init_y_dot: 0 + init_z: 1.4 + init_z_dot: 0 + init_phi: 0 + init_theta: 0 + init_psi: 0 + init_p: 0 + init_q: 0 + init_r: 0 + randomized_init: True + randomized_inertial_prop: False + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -2 + high: 2 + init_x_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_y: + distrib: 'uniform' + low: -2 + high: 2 + init_y_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_z: + distrib: 'uniform' + low: 0.3 # Just so it doesn't crash into the ground + high: 2 + init_z_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -1 + high: 1 + init_q: + distrib: 'uniform' + low: -1 + high: 1 + init_r: + distrib: 'uniform' + low: -1 + high: 1 + + task: stabilization + task_info: + stabilization_goal: [0, 0, 1] + stabilization_goal_tolerance: 0.0 + + inertial_prop: + M: 0.027 + Ixx: 1.4e-5 + Iyy: 1.4e-5 + Izz: 2.17e-5 + + episode_len_sec: 5 + cost: rl_reward + obs_goal_horizon: 0 + + # RL Reward + rew_state_weight: [1, 0.01, 1, 0.01, 1, 0.01, 0.5, 0.5, 0.5, 0.01, 0.01, 0.01] + rew_act_weight: 0.0001 + rew_exponential: True + info_mse_metric_state_weight: [1, 0.01, 1, 0.01, 1, 0.01, 0.5, 0.5, 0.5, 0.01, 0.01, 0.01] + + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 1 + - 2 + - 1 + - 2 + - 1 + - 0.2 + - 0.2 + - 0.2 + - 1 + - 1 + - 1 + lower_bounds: + - -2 + - -1 + - -2 + - -1 + - 0 + - -1 + - -0.2 + - -0.2 + - -0.2 + - -1 + - -1 + - -1 + - constraint_form: default_constraint + constrained_variable: input + upper_bounds: + - 0.148 + - 0.148 + - 0.148 + - 0.148 + lower_bounds: + - 0.029 + - 0.029 + - 0.029 + - 0.029 + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_track.yaml b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_track.yaml new file mode 100644 index 000000000..813d94a74 --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_track.yaml @@ -0,0 +1,144 @@ +task_config: + seed: 1337 + info_in_reset: True + ctrl_freq: 50 + pyb_freq: 1000 + physics: pyb + quad_type: 3 + normalized_rl_action_space: True + + init_state: + init_x: 0.4 + init_x_dot: 0 + init_y: 0.4 + init_y_dot: 0 + init_z: 1.4 + init_z_dot: 0 + init_phi: 0 + init_theta: 0 + init_psi: 0 + init_p: 0 + init_q: 0 + init_r: 0 + randomized_init: True + randomized_inertial_prop: False + + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -2 + high: 2 + init_x_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_y: + distrib: 'uniform' + low: -2 + high: 2 + init_y_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_z: + distrib: 'uniform' + low: 0.3 # Just so it doesn't crash into the ground + high: 2 + init_z_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -1 + high: 1 + init_q: + distrib: 'uniform' + low: -1 + high: 1 + init_r: + distrib: 'uniform' + low: -1 + high: 1 + + task: traj_tracking + task_info: + trajectory_type: figure8 + num_cycles: 1 + trajectory_plane: 'xz' + trajectory_position_offset: [0, 1] + trajectory_scale: 1 + proj_point: [0, 0, 0.5] + proj_normal: [0, 1, 1] + + inertial_prop: + M: 0.027 + Ixx: 1.4e-5 + Iyy: 1.4e-5 + Izz: 2.17e-5 + + episode_len_sec: 5 + cost: rl_reward + obs_goal_horizon: 1 + + # RL Reward + rew_state_weight: [1, 0.01, 1, 0.01, 1, 0.01, 0.5, 0.5, 0.5, 0.01, 0.01, 0.01] + rew_act_weight: 0.0001 + rew_exponential: True + info_mse_metric_state_weight: [1, 0.01, 1, 0.01, 1, 0.01, 0.5, 0.5, 0.5, 0.01, 0.01, 0.01] + + constraints: + - constraint_form: default_constraint + constrained_variable: state + upper_bounds: + - 2 + - 1 + - 2 + - 1 + - 2 + - 1 + - 0.2 + - 0.2 + - 0.2 + - 1 + - 1 + - 1 + lower_bounds: + - -2 + - -1 + - -2 + - -1 + - 0 + - -1 + - -0.2 + - -0.2 + - -0.2 + - -1 + - -1 + - -1 + - constraint_form: default_constraint + constrained_variable: input + upper_bounds: + - 0.148 + - 0.148 + - 0.148 + - 0.148 + lower_bounds: + - 0.029 + - 0.029 + - 0.029 + - 0.029 + done_on_out_of_bound: True + done_on_violation: False diff --git a/examples/rl/config_overrides/quadrotor_3D/sac_quadrotor_3D.yaml b/examples/rl/config_overrides/quadrotor_3D/sac_quadrotor_3D.yaml new file mode 100644 index 000000000..0ee2b2325 --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/sac_quadrotor_3D.yaml @@ -0,0 +1,29 @@ +algo: sac +algo_config: + # model args + hidden_dim: 128 + use_entropy_tuning: False + + # optim args + train_interval: 100 + train_batch_size: 256 + actor_lr: 0.001 + critic_lr: 0.001 + entropy_lr: 0.001 + + # runner args + max_env_steps: 400000 + warm_up_steps: 1000 + rollout_batch_size: 4 + num_workers: 1 + max_buffer_size: 1000000 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 4000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 4000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D.yaml b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D.yaml new file mode 100644 index 000000000..980be22cf --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D.yaml @@ -0,0 +1,33 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: False + entropy_coef: 0.01 + + # Safety layer args + pretraining: False + pretrained: null + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01] + constraint_hidden_dim: 150 + + # optim args + opt_epochs: 20 + mini_batch_size: 256 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 1000000 + rollout_batch_size: 4 + rollout_steps: 1000 + + # misc + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D_pretrain.yaml b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D_pretrain.yaml new file mode 100644 index 000000000..994243f3f --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D_pretrain.yaml @@ -0,0 +1,40 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: False + entropy_coef: 0.01 + + # Safety layer args + pretraining: True + pretrained: null + constraint_hidden_dim: 150 + constraint_lr: 0.0001 + constraint_batch_size: 256 + constraint_steps_per_epoch: 3000 + constraint_epochs: 500 + constraint_eval_steps: 1500 + constraint_eval_interval: 5 + constraint_buffer_size: 1000000 + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01] + + # optim args + opt_epochs: 20 + mini_batch_size: 256 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 1000000 + rollout_batch_size: 4 + rollout_steps: 1000 + + # misc + log_interval: 100 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 100 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/models/ppo/ppo_model_cartpole_stab.pt 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+from safe_control_gym.utils.registration import make + + +def run(gui=False, plot=True, n_episodes=1, n_steps=None, curr_path='.'): + '''Main function to run RL experiments. + + Args: + gui (bool): Whether to display the gui. + plot (bool): Whether to plot graphs. + n_episodes (int): The number of episodes to execute. + n_steps (int): How many steps to run the experiment. + curr_path (str): The current relative path to the experiment folder. + + Returns: + X_GOAL (np.ndarray): The goal (stabilization or reference trajectory) of the experiment. + results (dict): The results of the experiment. + metrics (dict): The metrics of the experiment. + ''' + + # Create the configuration dictionary. + fac = ConfigFactory() + config = fac.merge() + + task = 'stab' if config.task_config.task == Task.STABILIZATION else 'track' + if config.task == Environment.QUADROTOR: + system = f'quadrotor_{str(config.task_config.quad_type)}D' + else: + system = config.task + + env_func = partial(make, + config.task, + **config.task_config) + env = env_func(gui=gui) + + # Setup controller. + ctrl = make(config.algo, + env_func, + **config.algo_config, + output_dir=curr_path + '/temp') + + # Load state_dict from trained. + ctrl.load(f'{curr_path}/models/{config.algo}/{config.algo}_model_{system}_{task}.pt') + + # Remove temporary files and directories + shutil.rmtree(f'{curr_path}/temp', ignore_errors=True) + + # Run experiment + experiment = BaseExperiment(env, ctrl) + results, metrics = experiment.run_evaluation(n_episodes=n_episodes, n_steps=n_steps) + ctrl.close() + + if plot is True: + if system == Environment.CARTPOLE: + graph1_1 = 2 + graph1_2 = 3 + graph3_1 = 0 + graph3_2 = 1 + elif system == 'quadrotor_2D': + graph1_1 = 4 + graph1_2 = 5 + graph3_1 = 0 + graph3_2 = 2 + elif system == 'quadrotor_3D': + graph1_1 = 6 + graph1_2 = 9 + graph3_1 = 0 + graph3_2 = 4 + + _, ax = plt.subplots() + ax.plot(results['obs'][0][:, graph1_1], results['obs'][0][:, graph1_2], 'r--', label='RL Trajectory') + ax.scatter(results['obs'][0][0, graph1_1], results['obs'][0][0, graph1_2], color='g', marker='o', s=100, label='Initial State') + ax.set_xlabel(r'$\theta$') + ax.set_ylabel(r'$\dot{\theta}$') + ax.set_box_aspect(0.5) + ax.legend(loc='upper right') + + if config.task_config.task == Task.TRAJ_TRACKING and config.task == Environment.CARTPOLE: + _, ax2 = plt.subplots() + ax2.plot(np.linspace(0, 20, results['obs'][0].shape[0]), results['obs'][0][:, 0], 'r--', label='RL Trajectory') + ax2.plot(np.linspace(0, 20, results['obs'][0].shape[0]), env.X_GOAL[:, 0], 'b', label='Reference') + ax2.set_xlabel(r'Time') + ax2.set_ylabel(r'X') + ax2.set_box_aspect(0.5) + ax2.legend(loc='upper right') + elif config.task == Environment.QUADROTOR: + _, ax2 = plt.subplots() + ax2.plot(results['obs'][0][:, graph3_1 + 1], results['obs'][0][:, graph3_2 + 1], 'r--', label='RL Trajectory') + ax2.set_xlabel(r'x_dot') + ax2.set_ylabel(r'z_dot') + ax2.set_box_aspect(0.5) + ax2.legend(loc='upper right') + + _, ax3 = plt.subplots() + ax3.plot(results['obs'][0][:, graph3_1], results['obs'][0][:, graph3_2], 'r--', label='RL Trajectory') + if config.task_config.task == Task.TRAJ_TRACKING and config.task == Environment.QUADROTOR: + ax3.plot(env.X_GOAL[:, graph3_1], env.X_GOAL[:, graph3_2], 'g--', label='Reference') + ax3.scatter(results['obs'][0][0, graph3_1], results['obs'][0][0, graph3_2], color='g', marker='o', s=100, label='Initial State') + ax3.set_xlabel(r'X') + if config.task == Environment.CARTPOLE: + ax3.set_ylabel(r'Vel') + elif config.task == Environment.QUADROTOR: + ax3.set_ylabel(r'Z') + ax3.set_box_aspect(0.5) + ax3.legend(loc='upper right') + + plt.tight_layout() + plt.show() + + return env.X_GOAL, results, metrics + + +if __name__ == '__main__': + run() diff --git a/examples/rl/rl_experiment.sh b/examples/rl/rl_experiment.sh new file mode 100755 index 000000000..203bc14e4 --- /dev/null +++ b/examples/rl/rl_experiment.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +# SYS='cartpole' +# SYS='quadrotor_2D' +SYS='quadrotor_3D' + +# TASK='stab' +TASK='track' + +ALGO='ppo' +# ALGO='sac' +# ALGO='safe_explorer_ppo' + +if [ "$SYS" == 'cartpole' ]; then + SYS_NAME=$SYS +else + SYS_NAME='quadrotor' +fi + +# RL Experiment +python3 ./rl_experiment.py \ + --task ${SYS_NAME} \ + --algo ${ALGO} \ + --overrides \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + ./config_overrides/${SYS}/${ALGO}_${SYS}.yaml \ + --kv_overrides \ + algo_config.training=False \ + task_config.randomized_init=False diff --git a/examples/rl/train_rl_model.sh b/examples/rl/train_rl_model.sh new file mode 100755 index 000000000..d9647def7 --- /dev/null +++ b/examples/rl/train_rl_model.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +SYS='cartpole' +# SYS='quadrotor_2D' +# SYS='quadrotor_3D' + +TASK='stab' +# TASK='track' + +ALGO='ppo' +# ALGO='sac' +# ALGO='safe_explorer_ppo' + +if [ "$SYS" == 'cartpole' ]; then + SYS_NAME=$SYS +else + SYS_NAME='quadrotor' +fi + +# Removed the temporary data used to train the new unsafe model. +rm -r -f ./unsafe_rl_temp_data/ + +if [ "$ALGO" == 'safe_explorer_ppo' ]; then + # Pretrain the unsafe controller/agent. + python3 ../../safe_control_gym/experiments/train_rl_controller.py \ + --algo ${ALGO} \ + --task ${SYS_NAME} \ + --overrides \ + ./config_overrides/${SYS}/${ALGO}_${SYS}_pretrain.yaml \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + --output_dir ./unsafe_rl_temp_data/ \ + --seed 2 \ + --kv_overrides \ + task_config.init_state=None + + # Move the newly trained unsafe model. + mv ./unsafe_rl_temp_data/model_latest.pt ./models/${ALGO}/${ALGO}_pretrain_${SYS}_${TASK}.pt + + # Removed the temporary data used to train the new unsafe model. + rm -r -f ./unsafe_rl_temp_data/ +fi + +# Train the unsafe controller/agent. +python3 ../../safe_control_gym/experiments/train_rl_controller.py \ + --algo ${ALGO} \ + --task ${SYS_NAME} \ + --overrides \ + ./config_overrides/${SYS}/${ALGO}_${SYS}.yaml \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + --output_dir ./unsafe_rl_temp_data/ \ + --seed 2 \ + --kv_overrides \ + task_config.init_state=None \ + task_config.randomized_init=True \ + algo_config.pretrained=./models/${ALGO}/${ALGO}_pretrain_${SYS}_${TASK}.pt + +# Move the newly trained unsafe model. +mv ./unsafe_rl_temp_data/model_best.pt ./models/${ALGO}/${ALGO}_model_${SYS}_${TASK}.pt + +# Removed the temporary data used to train the new unsafe model. +rm -r -f ./unsafe_rl_temp_data/ diff --git a/examples/tutorial.md b/examples/tutorial.md new file mode 100644 index 000000000..ce7bf8b5e --- /dev/null +++ b/examples/tutorial.md @@ -0,0 +1,153 @@ +# Safe Control Gym - How to Get Started + +Refer to [this paper](https://arxiv.org/abs/2109.06325) for information on the motivation behind this gym as well as details on the implementation. + +## Running a basic experiment + +To run an experiment in safe-control-gym, the elements required are: + +1. A control approach or algorithm - choose an existing implementation of a control approaches (see the Control Approaches section for a list) or implement your own controller +2. A robotic model - choose a robotic model (cartpole, 2D or 1D quadrotor) or implement your own +3. (Optionally) A safety filter - choose an existing implementation of a safety filter to guarantee the safety of system (see the Safety Filters section for a list) or implement your own +4. Configuration files - these provide all the relevant information for the control approach, environment, and task + +See the examples in the `examples/` folder for example experiments using different controllers, environments, tasks, and safety filters. Let us see the necessary steps below: + +### Step 1: Create the folder and files +In `experiments/`, create a new folder for your experiment. Then, add a blank `.sh` file for running your experiment, a python file for the experiment called `.py`, and a folder called `config_overrides` for all the YAML override files you need. + +### Step 2: Create required configuraton files +The environment (which includes the task) and the controller must be configured. These may be for various experiments that you wish to conduct, and can all be collected in the `config_overrides` folder of your experiment. See `examples/lqr/config_overrides` for an example. + +### Step 3: Create basic shell script +Fill out `.sh`, the simple shell script defining the command to run the experiment. An example can be found in `examples/lqr/lqr_experiment.sh`. This script should, at minimum, follow the structure: +```bash +#!/bin/bash + +# . +python3 ${EXPERIMENT_NAME}.py \ + --task ${SYSTEM_NAME} \ + --algo ${CONTROLLER} \ + --overrides \ + ./config_overrides/${RELATIVE_PATH_TO_OVERRIDE_1} \ + ./config_overrides/${RELATIVE_PATH_TO_OVERRIDE_2} \ + ... +``` + +### Step 4: Merge configuration +Now we can write the python experiment file, `.py`. In the main function, we will start by merging the config that has been defined using the system, controller, and overrides in the shell script. +```python + # Create the configuration dictionary. + CONFIG_FACTORY = ConfigFactory() + config = CONFIG_FACTORY.merge() +``` + +### Step 5: Create environment and controller +Using the `config` dictionary, we can create the environment and controller. +```python + # Create an environment + env_func = partial(make, + config.task, + **config.task_config + ) + + # Create controller. + ctrl = make(config.algo, + env_func, + **config.algo_config + ) +``` + +### Step 6: Train the controller +If the controller requires training, or loading of a pre-trained model, this can be done now. Each controller is trained differently, so consult the source code for the controller. + + +### Step 7: Run the experiment +Using the `BaseExperiment` class, we can run our desired experiment. +```python + # Run the experiment. + experiment = BaseExperiment(ctrl.env, ctrl) + trajs_data, metrics = experiment.run_evaluation(n_episodes=1) + experiment.close() +``` +This will return standard data on the trajectory, and metrics calculated over that experiment. Consult the `BaseExperiment` class for more details. The data from the experiment can be displayed or saved in any way you like. To run the experiment, simply run `./.sh` in the terminal. + +## Using configuration/override files + +Each controller has a default configuration file with the bare-bones parameters initialized. These are stored in `safe-control-gym/safe_control_gym/controllers` with the implementations of the controllers. Here is a good place to start if you want to know more about how to start working with a controller. To add to and modify the existing configuration, an override file is used. These configurations are merged together using the `.merge()` method. + +### Command Line Options + +The configuration used by the experiment is specified via the command line: + +1. --algo : this specifies the default configuration for controller/algorithm used in the experiment +2. --task : this specifies the default configuration associated with the agent model used in the experiment +3. --safety_filter : this specifies the default configuration associated with the safety filter used in the experiment, if any are used. +4. --overrides : this specifies an overrides file that makes an desired modifications to the default configuration files you are using +5. --kv_overrides : this allows overriding specific values in the config using their key +6. --your_arg : using the `.add_argument()` method from `ConfigFactory()` in code and commandline arguments + +### Override Configurations + +For an experiment, you will need to change and specify some parameters using the default as a starting point. To do this, we specify the algorithm and task configurations in the override file loaded via the command line. + +Here is one way of setting up your overrides file. Open up `safe-control-gym/examples/lqr/config_overrides/` for an example. + +- task_config: + - Contains all task override configurations including: + - control frequency, constraints, disturbances, cost, initial state etc. +- algo_config: + - Contains all algorithm configurations including: + - seed, initial model parameters, number of iterations, learning rate etc. +- sf_config: + - Contains all safety filter configurations + +## Existing Control Approaches + +### Control and Safe Control Baselines + +| Approach | id | Location | +| -------- | --- | ----------- | +| PID Controller | 'pid' | [PID](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/controllers/pid/pid.py) | +| Linear Quadratic Regulator | 'lqr' | coming soon | +| Iterative Linear Quadratic Regulator | 'ilqr' | coming soon | + +### Reinforcement Learning Baselines + +| Approach | id | Location | +| -------- | --- | ----------- | +| Proximal Policy Optimization | 'ppo' | [PPO](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/controllers/ppo/ppo.py) | +| Soft-Actor Critic | 'sac' | [SAC](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/controllers/sac/sac.py) | + +### Safe Learning-based Control +| Approach | id | Location | +| -------- | --- | ----------- | +| Model Predictive Control w/ a Gaussian Process Model | 'gp_mpc' | [GP-MPC](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/controllers/mpc/gp_mpc.py) | +| Linear Model Predictive Control | 'linear_mpc' | [Linear MPC](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/controllers/mpc/linear_mpc.py) | + +### Safe and Robust Reinforcement Learning +| Approach | id | Location | +| -------- | --- | ----------- | +| Robust Adversarial Reinforcement Learning | 'rarl' | [RARL](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/controllers/rarl/rarl.py) | +| Robust Adversarial Reinforcement Learning using Adversarial Populations | 'rap' | [RAP](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/controllers/rarl/rap.py) | + +### Safety Filters +| Approach | id | Location | +| -------- | --- | ----------- | +| Model Predictive Safety Certification | 'linear_mpsc' | [MPSC](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/safety_filters/mpsc/mpsc.py) | +| Control Barrier Functions | 'cbf' | [CBF](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/safety_filter/cbf/cbf.py) | +| Neural Network Control Barrier Functions | 'cbf_nn' | [CBF](https://github.com/utiasDSL/safe-control-gym/blob/main/safe_control_gym/safety_filter/cbf/cbf_nn.py) | + +#### Safe Exploration +| Approach | id | Location | +| -------- | --- | ----------- | +| Safety Layer | 'safe_explorer_ppo' | [Safety Layer](https://github.com/utiasDSL/safe-control-gym/tree/main/safe_control_gym/controllers/safe_explorer) | + +## Adding a New Controller +- Create a new folder in `safe-control-gym/controllers` with the name of your controller +- Add a blank `__init__.py` file, a file for the base code of your controller called `.py`, and a YAML file for the standard configuration for your controller called `.yaml`. +- In `.py` extend the `BaseController` class found in `safe-control-gym/controllers/base_controller.py` to implement your controller. If necessary, create extra utility files called `_utils.py` in that same folder. +- In the YAML file, put in the basic configuration needed for your controller, which will be the variables in the instantiation of your controller object. +- In `safe-control-gym/controllers/__init__.py`, register your new controller by adding a new registration command. + +See `safe_control_gym/controllers/pid/` for a simple example. diff --git a/figures/baselines.png b/figures/baselines.png new file mode 100644 index 000000000..d355b3ee1 Binary files /dev/null and b/figures/baselines.png differ diff --git a/poetry.lock b/poetry.lock index 381c75db4..70f7ab336 100644 --- a/poetry.lock +++ b/poetry.lock @@ -30,6 +30,24 @@ tests = ["attrs", "zope-interface"] tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] tests-no-zope = ["attrs", "cloudpickle", "hypothesis", "pympler", "pytest-xdist", "pytest (>=4.3.0)"] +[[package]] +name = "beautifulsoup4" +version = "4.12.3" +description = "Screen-scraping library" +category = "main" +optional = false +python-versions = ">=3.6.0" + +[package.dependencies] +soupsieve = ">1.2" + +[package.extras] +cchardet = ["cchardet"] +chardet = ["chardet"] +charset-normalizer = ["charset-normalizer"] +html5lib = ["html5lib"] +lxml = ["lxml"] + [[package]] name = "casadi" version = "3.6.5" @@ -41,6 +59,14 @@ python-versions = "*" [package.dependencies] numpy = "*" +[[package]] +name = "certifi" +version = "2024.2.2" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "cfgv" version = "3.4.0" @@ -49,6 +75,14 @@ category = "main" optional = false python-versions = ">=3.8" +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." +category = "main" +optional = false +python-versions = ">=3.7.0" + [[package]] name = "clarabel" version = "0.7.1" @@ -63,11 +97,11 @@ scipy = "*" [[package]] name = "click" -version = "8.0.4" +version = "8.1.7" description = "Composable command line interface toolkit" category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] colorama = {version = "*", markers = "platform_system == \"Windows\""} @@ -247,6 +281,23 @@ ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=15.1.0)"] woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +[[package]] +name = "gdown" +version = "5.1.0" +description = "Google Drive Public File/Folder Downloader" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +beautifulsoup4 = "*" +filelock = "*" +requests = {version = "*", extras = ["socks"]} +tqdm = "*" + +[package.extras] +test = ["build", "mypy", "pytest", "pytest-xdist", "ruff", "twine", "types-requests"] + [[package]] name = "gpytorch" version = "1.11" @@ -319,6 +370,14 @@ python-versions = ">=3.8" [package.extras] license = ["ukkonen"] +[[package]] +name = "idna" +version = "3.6" +description = "Internationalized Domain Names in Applications (IDNA)" +category = "main" +optional = false +python-versions = ">=3.5" + [[package]] name = "imageio" version = "2.34.0" @@ -488,15 +547,12 @@ python-dateutil = ">=2.7" [[package]] name = "mosek" -version = "9.3.22" -description = "Python interface for MOSEK - the package for large scale convex, conic and mixed-integer optimization." +version = "10.0.47" +description = "Python API for Mosek" category = "main" optional = false python-versions = "*" -[package.dependencies] -numpy = "*" - [[package]] name = "msgpack" version = "1.0.8" @@ -520,6 +576,21 @@ six = "*" testing = ["pytest", "coverage", "astroid (>=1.5.3,<1.6.0)", "pylint (>=1.7.2,<1.8.0)", "astroid (>=2.0)", "pylint (>=2.3.1,<2.4.0)"] yaml = ["PyYAML (>=5.1.0)"] +[[package]] +name = "networkx" +version = "3.2.1" +description = "Python package for creating and manipulating graphs and networks" +category = "main" +optional = false +python-versions = ">=3.9" + +[package.extras] +default = ["numpy (>=1.22)", "scipy (>=1.9,!=1.11.0,!=1.11.1)", "matplotlib (>=3.5)", "pandas (>=1.4)"] +developer = ["changelist (==0.4)", "pre-commit (>=3.2)", "mypy (>=1.1)", "rtoml"] +doc = ["sphinx (>=7)", "pydata-sphinx-theme (>=0.14)", "sphinx-gallery (>=0.14)", "numpydoc (>=1.6)", "pillow (>=9.4)", "nb2plots (>=0.7)", "texext (>=0.6.7)", "nbconvert (<7.9)"] +extra = ["lxml (>=4.6)", "pygraphviz (>=1.11)", "pydot (>=1.4.2)", "sympy (>=1.10)"] +test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"] + [[package]] name = "nodeenv" version = "1.8.0" @@ -663,6 +734,19 @@ python-versions = ">=3.8" dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] +[[package]] +name = "polytope" +version = "0.2.5" +description = "Polytope Toolbox" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +networkx = ">=3.0" +numpy = ">=1.24.1" +scipy = ">=1.10.0" + [[package]] name = "pre-commit" version = "3.7.0" @@ -680,11 +764,11 @@ virtualenv = ">=20.10.0" [[package]] name = "protobuf" -version = "3.20.3" -description = "Protocol Buffers" +version = "5.26.1" +description = "" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" [[package]] name = "pyaml" @@ -719,6 +803,14 @@ category = "main" optional = false python-versions = "*" +[[package]] +name = "pycddlib" +version = "2.1.7" +description = "pycddlib is a Python wrapper for Komei Fukuda's cddlib." +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "pygments" version = "2.17.2" @@ -742,6 +834,14 @@ python-versions = ">=3.6.8" [package.extras] diagrams = ["railroad-diagrams", "jinja2"] +[[package]] +name = "pysocks" +version = "1.7.1" +description = "A Python SOCKS client module. See https://github.com/Anorov/PySocks for more information." +category = "main" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + [[package]] name = "pytest" version = "8.1.1" @@ -772,6 +872,20 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" [package.dependencies] six = ">=1.5" +[[package]] +name = "pytope" +version = "0.0.4" +description = "Polytope operations --- limited functionality" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +matplotlib = "*" +numpy = "*" +pycddlib = "*" +scipy = ">=1.3.0" + [[package]] name = "pytz" version = "2024.1" @@ -858,6 +972,25 @@ python-versions = ">=3.8" attrs = ">=22.2.0" rpds-py = ">=0.7.0" +[[package]] +name = "requests" +version = "2.31.0" +description = "Python HTTP for Humans." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +certifi = ">=2017.4.17" +charset-normalizer = ">=2,<4" +idna = ">=2.5,<4" +PySocks = {version = ">=1.5.6,<1.5.7 || >1.5.7", optional = true, markers = "extra == \"socks\""} +urllib3 = ">=1.21.1,<3" + +[package.extras] +socks = ["PySocks (>=1.5.6,!=1.5.7)"] +use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] + [[package]] name = "rich" version = "12.6.0" @@ -955,6 +1088,14 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" +[[package]] +name = "soupsieve" +version = "2.5" +description = "A modern CSS selector implementation for Beautiful Soup." +category = "main" +optional = false +python-versions = ">=3.8" + [[package]] name = "tensorboard" version = "2.16.2" @@ -1023,6 +1164,23 @@ typing-extensions = "*" [package.extras] opt-einsum = ["opt-einsum (>=3.3)"] +[[package]] +name = "tqdm" +version = "4.66.2" +description = "Fast, Extensible Progress Meter" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +colorama = {version = "*", markers = "platform_system == \"Windows\""} + +[package.extras] +dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"] +notebook = ["ipywidgets (>=6)"] +slack = ["slack-sdk"] +telegram = ["requests"] + [[package]] name = "typeguard" version = "2.13.3" @@ -1043,6 +1201,20 @@ category = "main" optional = false python-versions = ">=3.8" +[[package]] +name = "urllib3" +version = "2.2.1" +description = "HTTP library with thread-safe connection pooling, file post, and more." +category = "main" +optional = false +python-versions = ">=3.8" + +[package.extras] +brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] +h2 = ["h2 (>=4,<5)"] +socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] +zstd = ["zstandard (>=0.18.0)"] + [[package]] name = "virtualenv" version = "20.25.1" @@ -1077,14 +1249,17 @@ watchdog = ["watchdog (>=2.3)"] [metadata] lock-version = "1.1" python-versions = "^3.10" -content-hash = "42235a93c18acd371dd5a2a17b5bf432e4d434923e328f5db4b494357f1e6ff0" +content-hash = "8502891ae5a6a284fc7710cebd0a64c26bad8f58293e4eb5b4630ae6c8179be5" [metadata.files] absl-py = [] async-timeout = [] attrs = [] +beautifulsoup4 = [] casadi = [] +certifi = [] cfgv = [] +charset-normalizer = [] clarabel = [] click = [] cloudpickle = [] @@ -1100,10 +1275,12 @@ exceptiongroup = [] farama-notifications = [] filelock = [] fonttools = [] +gdown = [] gpytorch = [] grpcio = [] gymnasium = [] identify = [] +idna = [] imageio = [] iniconfig = [] jax-jumpy = [] @@ -1119,6 +1296,7 @@ matplotlib = [] mosek = [] msgpack = [] munch = [] +networkx = [] nodeenv = [] numpy = [] nvidia-cublas-cu11 = [] @@ -1132,21 +1310,26 @@ pandas = [] pillow = [] platformdirs = [] pluggy = [] +polytope = [] pre-commit = [] protobuf = [] pyaml = [] pybind11 = [] pybullet = [] +pycddlib = [] pygments = [] pyparsing = [] +pysocks = [] pytest = [] python-dateutil = [] +pytope = [] pytz = [] pyyaml = [] qdldl = [] ray = [] redis = [] referencing = [] +requests = [] rich = [] rpds-py = [] scikit-learn = [] @@ -1154,13 +1337,16 @@ scikit-optimize = [] scipy = [] scs = [] six = [] +soupsieve = [] tensorboard = [] tensorboard-data-server = [] termcolor = [] threadpoolctl = [] tomli = [] torch = [] +tqdm = [] typeguard = [] typing-extensions = [] +urllib3 = [] virtualenv = [] werkzeug = [] diff --git a/pyproject.toml b/pyproject.toml index 7eade9752..0b8c7dff5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,17 +20,20 @@ pandas = "^1.4.0" torch = "^1.10.2" gpytorch = "^1.6.0" ray = "^1.9.2" -tensorboard = "^2.8.0" -casadi = "^3.5.5" +tensorboard = "^2.12.0" +casadi = "^3.6.0" pybullet = "^3.2.0" cvxpy = "^1.1.18" numpy = "^1.22.1" -Mosek = "^9.3.14" +Mosek = "^10.0.18" termcolor = "^1.1.0" rich = "^12.5.1 " opencv-python = "^4.8.0" pytest = "^8.1.1" pre-commit = "^3.7.0" +polytope = "^0.2.5" +pytope = "^0.0.4" +gdown = "^5.1.0" [tool.poetry.dev-dependencies] diff --git a/safe_control_gym/__init__.py b/safe_control_gym/__init__.py index e797863f6..28b7acf2c 100644 --- a/safe_control_gym/__init__.py +++ b/safe_control_gym/__init__.py @@ -1,3 +1,5 @@ from safe_control_gym.controllers import * from safe_control_gym.envs import * from safe_control_gym.envs.env_wrappers import * +from safe_control_gym.experiments import * +from safe_control_gym.safety_filters import * diff --git a/safe_control_gym/controllers/base_controller.py b/safe_control_gym/controllers/base_controller.py index 187a965bc..dafd5e666 100644 --- a/safe_control_gym/controllers/base_controller.py +++ b/safe_control_gym/controllers/base_controller.py @@ -1,90 +1,194 @@ -"""Base classes. +'''Base controller.''' + +from abc import ABC, abstractmethod -""" import torch -class BaseController: - """Template for controller/agent, implement the following methods as needed. - """ +class BaseController(ABC): + '''Template for controller/agent, implement the following methods as needed.''' def __init__(self, env_func, training=True, - checkpoint_path="temp/model_latest.pt", - output_dir="temp", + checkpoint_path='temp/model_latest.pt', + output_dir='temp', use_gpu=False, seed=0, **kwargs ): - """Initializes controller agent. + '''Initializes controller agent. Args: - env_func (callable): function to instantiate task/env. - training (bool): training flag. - checkpoint_path (str): file to save trained model & experiment state. - output_dir (str): folder to write outputs. + env_func (callable): Function to instantiate task/env. + training (bool): Training flag. + checkpoint_path (str): File to save trained model & experiment state. + output_dir (str): Folder to write outputs. use_gpu (bool): False (use cpu) True (use cuda). - seed (int): random seed. + seed (int): Random seed. + ''' - """ # Base args. self.env_func = env_func self.training = training self.checkpoint_path = checkpoint_path self.output_dir = output_dir self.use_gpu = use_gpu and torch.cuda.is_available() - self.device = 'cpu' if self.use_gpu == False else 'cuda' + self.device = 'cpu' if self.use_gpu is False else 'cuda' self.seed = seed + self.prior_info = {} + # Algorithm specific args. - for k, v in kwargs.items(): - self.__dict__[k] = v + for key, value in kwargs.items(): + self.__dict__[key] = value + + self.setup_results_dict() + + @abstractmethod + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. + + Args: + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. + + Returns: + action (ndarray): The action chosen by the controller. + ''' + raise NotImplementedError + + def extract_step(self, info=None): + '''Extracts the current step from the info. + + Args: + info (dict): The info list returned from the environment. + + Returns: + step (int): The current step/iteration of the environment. + ''' + if info is not None: + step = info['current_step'] + else: + step = 0 + + return step + + def learn(self, + env=None, + **kwargs + ): + '''Performs learning (pre-training, training, fine-tuning, etc). + + Args: + env (BenchmarkEnv): The environment to be used for training. + ''' + return + + @abstractmethod def reset(self): - """Do initializations for training or evaluation. + '''Do initializations for training or evaluation.''' + raise NotImplementedError - """ - pass + def reset_before_run(self, obs=None, info=None, env=None): + '''Reinitialize just the controller before a new run. - def close(self): - """Shuts down and cleans up lingering resources. + Args: + obs (ndarray): The initial observation for the new run. + info (dict): The first info of the new run. + env (BenchmarkEnv): The environment to be used for the new run. + ''' + self.setup_results_dict() - """ - pass + @abstractmethod + def close(self): + '''Shuts down and cleans up lingering resources.''' + raise NotImplementedError def save(self, path ): - """Saves model params and experiment state to checkpoint path. + '''Saves model params and experiment state to checkpoint path. - """ - pass + Args: + path (str): The path where to save the model params/experiment state. + ''' + return def load(self, path ): - """Restores model and experiment given checkpoint path. + '''Restores model and experiment given checkpoint path. - """ - pass + Args: + path (str): The path where the model params/experiment state are saved. + ''' + return - def learn(self, - env=None, - **kwargs - ): - """Performs learning (pre-training, training, fine-tuning, etc). - - """ - pass - - def run(self, - env=None, - render=False, - n_episodes=10, - verbose=False, - **kwargs - ): - """Runs evaluation with current policy. - - """ - pass + def setup_results_dict(self): + '''Setup the results dictionary to store run information.''' + self.results_dict = {} + + def get_prior(self, env, prior_info={}): + '''Fetch the prior model from the env for the controller. + + Note there's a default env.symbolic when each each env is created. + To make a different prior model, do the following when initializing a ctrl:: + + self.env = env_func() + self.model = self.get_prior(self.env) + + Besides the env config `base.yaml` and ctrl config `mpc.yaml`, + you can define an additional prior config `prior.yaml` that looks like:: + + algo_config: + prior_info: + prior_prop: + M: 0.03 + Iyy: 0.00003 + randomize_prior_prop: False + prior_prop_rand_info: {} + + and to ensure the resulting config.algo_config contains both the params + from ctrl config and prior config, chain them to the --overrides like: + + python experiment.py --algo mpc --task quadrotor --overrides base.yaml mpc.yaml prior.yaml ... + + Also note we look for prior_info from the incoming function arg first, then the ctrl itself. + this allows changing the prior model during learning by calling:: + + new_model = self.get_prior(same_env, new_prior_info) + + Alternatively, you can overwrite this method and use your own format for prior_info + to customize how you get/change the prior model for your controller. + + Args: + env (BenchmarkEnv): the environment to fetch prior model from. + prior_info (dict): specifies the prior properties or other things to + overwrite the default prior model in the env. + + Returns: + SymbolicModel: CasAdi prior model. + ''' + if not prior_info: + prior_info = getattr(self, 'prior_info', {}) + prior_prop = prior_info.get('prior_prop', {}) + + # randomize prior prop, similar to randomizing the inertial_prop in BenchmarkEnv + # this can simulate the estimation errors in the prior model + randomize_prior_prop = prior_info.get('randomize_prior_prop', False) + prior_prop_rand_info = prior_info.get('prior_prop_rand_info', {}) + if randomize_prior_prop and prior_prop_rand_info: + # check keys, this is due to the current implementation of BenchmarkEnv._randomize_values_by_info() + for k in prior_prop_rand_info: + assert k in prior_prop, 'A prior param to randomize does not have a base value in prior_prop.' + prior_prop = env._randomize_values_by_info(prior_prop, prior_prop_rand_info) + + # Note we only reset the symbolic model when prior_prop is nonempty + if prior_prop: + env._setup_symbolic(prior_prop=prior_prop) + + # Note this ensures the env can still access the prior model, + # which is used to get quadratic costs in env.step() + prior_model = env.symbolic + return prior_model diff --git a/safe_control_gym/controllers/cbf/cbf.py b/safe_control_gym/controllers/cbf/cbf.py deleted file mode 100644 index fdd95a16f..000000000 --- a/safe_control_gym/controllers/cbf/cbf.py +++ /dev/null @@ -1,318 +0,0 @@ -"""Control barrier function (CBF) main - -Examples - check CBF candidate on cartpole balance: - - python3 cbf.py --func is_cbf --algo cbf --task cartpole --overrides ./cbf_qp_verify.yaml - - train on cartpole balance: - - python3 cbf.py --func train --algo cbf --task cartpole --overrides ./cbf_qp_learn.yaml - - test on cartpole balance: - - python3 cbf.py --func test --algo cbf --task cartpole --overrides ./cbf_qp_test.yaml - -""" - -from functools import partial -import os -import torch -import numpy as np -import matplotlib.pyplot as plt - -from safe_control_gym.utils.configuration import ConfigFactory -from safe_control_gym.utils.registration import make -from safe_control_gym.utils.utils import set_device_from_config - - -def plot_is_cbf(infeasible_states, maximum_states): - # Plot feasible and infeasible points - - state_ids = {"x_pos": 0, "x_dot": 1, "theta": 2, "theta_dot": 3} - max_states = {} - for i, state_id in enumerate(state_ids.keys()): - max_states[state_id] = maximum_states[i] - - phi = np.linspace(0, 2 * np.pi, num=120) - - plots = [["x_pos", "theta"], ["theta", "theta_dot"], ["x_pos", "x_dot"]] - - for i, plot in enumerate(plots): - if plot[0] == "theta" and plot[1] == "theta_dot": - plt.plot(max_states[plot[0]] * np.sin(phi), max_states[plot[1]] * np.cos(phi), label="superlevel set") - plt.xlabel(plot[0]) - plt.ylabel(plot[1]) - - if len(infeasible_states) > 0: - for index, infeasible_state in enumerate(infeasible_states): - if index == 0: - plt.plot(infeasible_state[state_ids[plot[0]]], infeasible_state[state_ids[plot[1]]], "rx", - label="infeasible state") - else: - plt.plot(infeasible_state[state_ids[plot[0]]], infeasible_state[state_ids[plot[1]]], "rx") - - plt.legend() - plt.show() - - -def plot_test(stats_buffer, maximum_states): - state_ids = {"state/x_pos": 0, "state/x_dot": 1, "state/theta": 2, "state/theta_dot": 3} - max_states = {} - for i, state_id in enumerate(state_ids.keys()): - max_states[state_id] = maximum_states[i] - - plots = [["t", ["action/safe_input", "action/unsafe_input", "action/applied_input"]], - ["state/theta", "state/theta_dot"], - ["state/x_pos", "state/x_dot"], - ["state/x_pos", "state/theta"]] - - t = range(len(stats_buffer[plots[0][1][0]])) - print("Num time steps:", len(t)) - - phi = np.linspace(0, 2 * np.pi, num=120) - line_styles = ["m-", "c-", "k--"] - - for plot in plots: - if plot[0] == "t": - plot_x = t - plt.xlabel("step count") - plt.ylabel("control input") - else: - plot_x = stats_buffer[plot[0]] - if plot[0] == "state/theta" and plot[1] == "state/theta_dot": - plt.plot(max_states[plot[0]] * np.sin(phi), max_states[plot[1]] * np.cos(phi), "b-", - label="superlevel set") - plt.xlabel(plot[0]) - plt.ylabel(plot[1]) - if isinstance(plot[1], list): - for index, plot_y_id in enumerate(plot[1]): - plt.plot(plot_x, stats_buffer[plot_y_id], line_styles[index], label=plot_y_id) - else: - plot_y = stats_buffer[plot[1]] - plt.plot(plot_x, plot_y) - plt.plot(plot_x[0], plot_y[0], 'gx', label="start") - plt.plot(plot_x[-1], plot_y[-1], 'rx', label="end") - - plt.legend() - plt.show() - - -def is_cbf(config): - """ - Check if the provided CBF candidate is a CBF for the true system and the a priori system. - """ - - # Evaluation setup - set_device_from_config(config) - - is_cbf = [False] * 2 - maximum_states = [config.algo_config.x_pos_max, - config.algo_config.x_vel_max, - config.algo_config.theta_max, - config.algo_config.theta_dot_max] - - # Check CBF for true system and the a priori system - for i in range(2): - if i == 0: - print("--------------------------------------------------------") - print("1. Check provided CBF candidate for the a priori system.") - print("--------------------------------------------------------") - elif i == 1: - print("--------------------------------------------------------") - print("2. Check provided CBf candidate for the true system.") - print("--------------------------------------------------------") - - # Define function to create task/env - env_func = partial(make, - config.task, - output_dir=config.output_dir, - # prior_prop=config.task_config.prior_prop, - **config.task_config) - - # Create the controller/control_agent. - control_agent = make(config.algo, - env_func, - training=False, - checkpoint_path=os.path.join(config.output_dir, - "model_latest.pt"), - output_dir=config.output_dir, - device=config.device, - seed=config.seed, - **config.algo_config) - - control_agent.reset() - - num_points = config.algo_config.num_points - tolerance = config.algo_config.tolerance - - is_cbf[i], infeasible_states = control_agent.is_cbf(num_points=num_points, tolerance=tolerance) - control_agent.close() - - plot_is_cbf(infeasible_states, maximum_states) - - if i == 0: - # Switch to the true system for the next check - config.task_config.prior_prop = None - - if is_cbf[0] and is_cbf[1]: - print("------------------------------------------------------------------------------------------") - print("The provided CBF candidate is potentially a CBF for both the true and the a priori system.") - print("------------------------------------------------------------------------------------------") - else: - print("------------------------------------------------------------------------------------------") - print("The provided CBF candidate is NOT a CBF for both the true and the a priori system.") - print("------------------------------------------------------------------------------------------") - - -def train(config): - """General training template. - - Usage: - * to start training, use with `--func train`. - * to restore from a previous training, additionally use `--restore {dir_path}` - where `dir_path` is the output folder from previous training. - - """ - # Evaluation setup - set_device_from_config(config) - - maximum_states = [config.algo_config.x_pos_max, - config.algo_config.x_vel_max, - config.algo_config.theta_max, - config.algo_config.theta_dot_max] - - # Define function to create task/env - env_func = partial(make, - config.task, - output_dir=config.output_dir, - **config.task_config) - - unsafe_control_agent = make(config.algo_config["unsafe_controller"], - env_func, - training=False, - checkpoint_path=os.path.join(config.output_dir, "model_latest.pt"), - output_dir=config.output_dir, - device=config.device, - seed=config.seed, - **config.algo_config) - - config.algo_config["unsafe_controller"] = unsafe_control_agent - - # Create the controller/control_agent. - control_agent = make(config.algo, - env_func, - training=False, - checkpoint_path=os.path.join(config.output_dir, - "model_latest.pt"), - output_dir=config.output_dir, - device=config.device, - seed=config.seed, - **config.algo_config) - - control_agent.reset() - if config.restore: - control_agent.load(os.path.join(config.restore, "model_latest.pt")) - - control_agent.learn() - control_agent.close() - - -def test_policy(config): - """Run the (trained) policy/controller for evaluation. - - Usage - * use with `--func test`. - * to test policy from a trained model checkpoint, additionally use - `--restore {dir_path}` where `dir_path` is folder to the trained model. - * to test un-trained policy (e.g. non-learning based), use as it is. - - """ - # Evaluation setup - set_device_from_config(config) - - maximum_states = [config.algo_config.x_pos_max, - config.algo_config.x_vel_max, - config.algo_config.theta_max, - config.algo_config.theta_dot_max] - - # Define function to create task/env - env_func = partial(make, - config.task, - output_dir=config.output_dir, - **config.task_config) - - unsafe_control_agent = make(config.algo_config["unsafe_controller"], - env_func, - training=False, - checkpoint_path=os.path.join(config.output_dir, "model_latest.pt"), - output_dir=config.output_dir, - device=config.device, - seed=config.seed, - **config.algo_config) - - config.algo_config["unsafe_controller"] = unsafe_control_agent - - # Create the controller/control_agent. - control_agent = make(config.algo, - env_func, - training=False, - checkpoint_path=os.path.join(config.output_dir, - "model_latest.pt"), - output_dir=config.output_dir, - device=config.device, - seed=config.seed, - **config.algo_config) - - control_agent.reset() - if config.restore: - control_agent.load(os.path.join(config.restore, "model_latest.pt")) - - stats_buffer = control_agent.run(logging=True) - control_agent.close() - - plot_test(stats_buffer, maximum_states) - - -MAIN_FUNCS = {"train": train, "test": test_policy, "is_cbf": is_cbf} - -if __name__ == "__main__": - # Make config - fac = ConfigFactory() - fac.add_argument("--func", - type=str, - default="train", - help="main function to run.") - fac.add_argument("--thread", - type=int, - default=0, - help="number of threads to use (set by torch).") - fac.add_argument("--render", - action="store_true", - help="if to render in policy test.") - fac.add_argument("--verbose", - action="store_true", - help="if to print states & actions in policy test.") - fac.add_argument("--use_adv", - action="store_true", - help="if to evaluate against adversary.") - fac.add_argument("--eval_output_path", - type=str, - default="test_results.pkl", - help="file path to save evaluation results.") - config = fac.merge() - - # system settings - if config.thread > 0: - # e.g. set single thread for less context switching - torch.set_num_threads(config.thread) - - # Execute - func = MAIN_FUNCS.get(config.func, None) - if func is None: - raise Exception("Main function {} not supported.".format(config.func)) - - if "cartpole" not in config.task: - raise Exception("Task {} not supported.".format(config.task)) - - func(config) diff --git a/safe_control_gym/controllers/cbf/cbf_qp.py b/safe_control_gym/controllers/cbf/cbf_qp.py deleted file mode 100644 index 8ded0d745..000000000 --- a/safe_control_gym/controllers/cbf/cbf_qp.py +++ /dev/null @@ -1,541 +0,0 @@ -"""Control barrier function (CBF) quadratic programming (QP) safety filter with learned Lie derivatives - -Reference: - * [Learning for Safety-Critical Control with Control Barrier Functions](https://arxiv.org/abs/1912.10099) - -""" -import os -from sys import platform -import numpy as np -import casadi as cs -import torch - -from safe_control_gym.controllers.base_controller import BaseController -from safe_control_gym.controllers.cbf.cbf_qp_utils import linear_function, cbf_cartpole, cartesian_product -from safe_control_gym.controllers.cbf.cbf_qp_utils import CBFBuffer -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics -from safe_control_gym.math_and_models.neural_networks import MLP -from safe_control_gym.utils.logging import ExperimentLogger - - -class CBF_QP(BaseController): - def __init__(self, - env_func, - unsafe_controller=None, - # Runner args. - deque_size=10, - eval_batch_size=1, - output_dir="results/temp", - **custom_args): - """ - CBF-QP controller with learned Lie derivative: The CBF's superlevel set defines a positively control invariant - set. A QP based on the CBF's Lie derivative with respect to the dynamics allows to filter arbitrary control - inputs to keep the system inside the CBF's superlevel set. Due to model mismatch, the Lie derivative is also - incorrect. This approach learns the error in the Lie derivative from multiple experiments to satisfy the - Lie derivative condition in the QP for the true system. - - Args: - env_func (gym.Env): Functionalized initialization of the environment. - unsafe_controller (BaseController): Underlying controller providing (unsafe) control inputs - deque_size (int): TODO - eval_batch_size(int): TODO - output_dir (str): TODO - - """ - - # TODO: Combine with CLF approach for stabilization - # TODO: Currently specific for cartpole! Make more general for other systems, e.g., extend to quadrotor - # environment - - # algo specific args - for k, v in custom_args.items(): - self.__dict__[k] = v - - self.eval_batch_size = eval_batch_size - - self.env = env_func() - self.step_size = self.env.PYB_FREQ // self.env.CTRL_FREQ - self.env = RecordEpisodeStatistics(self.env, deque_size) - - self.input_constraints_sym = self.env.constraints.get_input_constraint_symbolic_models() - - self.unsafe_controller = unsafe_controller - - self.model = self.env.symbolic - self.X = self.model.x_sym - self.u = self.model.u_sym - - # TODO: Extend this to systems that are not control affine. Then we would need to move away from a QP solver - # Check if the dynamics are control affine - assert self.is_control_affine() - - # Control barrier function - # TODO: Extend to other systems - self.cbf = cbf_cartpole(self.X, self.x_pos_max, self.x_vel_max, self.theta_max, self.theta_dot_max) - # Lie derivative with respect to the dynamics - self.lie_derivative = self.get_lie_derivative() - - self.linear_func = linear_function(self.slope) - # TODO: define two different linear functions to be steeper inside the safe set and flatter outside safe set - - # Neural network to learn the residual in the lie derivative - self.mlp = MLP(self.model.nx, self.model.nu + 1, hidden_dims=self.hidden_dims, activation="relu") - # optimizer - self.opt = torch.optim.Adam(self.mlp.parameters(), self.learning_rate) - - max_buffer_size = int(self.max_buffer_size) - self.buffer = CBFBuffer(self.env.observation_space, self.env.action_space, max_buffer_size, - self.train_batch_size) - - # Logging. - self.logger = ExperimentLogger(output_dir) - - def reset(self): - """Resets the environment - - """ - self.env.reset() - - def get_lie_derivative(self): - """Determines the Lie derivative of the CBF with respect to the known dynamics - - """ - dVdx = cs.gradient(self.cbf(X=self.X)['cbf'], self.X) - LfV = cs.dot(dVdx, self.model.x_dot) - LfV_func = cs.Function('LfV', [self.X, self.u], [LfV], ['X', 'u'], ['LfV']) - return LfV_func - - def is_control_affine(self): - """Check if the system is control affine - - """ - dfdu = cs.jacobian(self.model.x_dot, self.u) - return not cs.depends_on(dfdu, self.u) - - def is_cbf(self, num_points=100, tolerance=0.0): - """ - Check if the provided CBF candidate is actually a CBF for the system using a gridded approach - - Args: - num_points (int): The minimum number of points to check for the verification - tolerance (float): The amount by which the box that contains the grid is extended in every dimension - - Returns: - valid_cbf (bool): Whether the provided CBF candidate is valid - infeasible_states (list): List of all states for which the QP is infeasible - - """ - valid_cbf = False - - # Select the states to check the CBF condition - max_bounds = np.array([self.x_pos_max, self.x_vel_max, self.theta_max, self.theta_dot_max]) - # Add some tolerance to the bounds to also check the condition outside of the superlevel set - max_bounds += tolerance - min_bounds = - max_bounds - - # state dimension and input dimension - nx, nu = self.model.nx, self.model.nu - - # Make sure that every vertex is checked - num_points = max(2 * nx, num_points + num_points % (2 * nx)) - num_points_per_dim = num_points // nx - - # Create the lists of states to check - states_to_sample = [np.linspace(min_bounds[i], max_bounds[i], num_points_per_dim) for i in range(nx)] - states_to_check = cartesian_product(*states_to_sample) - - # Set dummy control input - control_input = np.ones((nu, 1)) - - num_infeasible = 0 - infeasible_states = [] - - # Check if the optimization problem is feasible for every considered state - for state in states_to_check: - # Certify action without using any learned model - safe_control_input, success = self.certify_action(state, control_input, use_learned_model=False) - if not success: - infeasible_states.append(state) - num_infeasible += 1 - - num_infeasible_states_inside_set = 0 - - # Check if the infeasible point is inside or outside the superlevel set. Note that the sampled region makes up a - # box, but the superlevel set is not. The superlevel set only needs to be contained inside the box. - for infeasible_state in infeasible_states: - barrier_at_x = self.cbf(X=infeasible_state)['cbf'] - if barrier_at_x < 0: - # print("Outside superlevel set:", infeasible_state) - pass - else: - print("Infeasible state inside superlevel set:", infeasible_state) - num_infeasible_states_inside_set += 1 - - print("Number of infeasible states:", num_infeasible) - print("Number of infeasible states inside superlevel set:", num_infeasible_states_inside_set) - - if num_infeasible_states_inside_set > 0: - valid_cbf = False - # print("The provided CBF candidate is not a valid CBF.") - elif num_infeasible > 0: - valid_cbf = True - # print("The provided CBF candidate is a valid CBF inside its superlevel set for the checked states. " - # "Consider increasing the sampling resolution to get a more precise evaluation. " - # "The CBF is not valid on the entire provided domain. Consider softening the CBF constraint by " - # "setting 'soft_constraint: True' inside the config.") - else: - valid_cbf = True - # print("The provided CBF candidate is a valid CBF for the checked states. " - # "Consider increasing the sampling resolution to get a more precise evaluation.") - - return valid_cbf, infeasible_states - - def certify_action(self, current_state, unsafe_action, use_learned_model=True): - """Calculates certified control input. - - Args: - current_state (np.array): current state of the continuous-time system - unsafe_action (np.array): unsafe control input. - use_learned_model (bool): Whether the learned Lie derivative is used in the certification - - Returns: - u_val (np.array): certified control input - success (bool): Whether the certification was successful - - """ - nx, nu = self.model.nx, self.model.nu - - # define optimizer and variables - opti = cs.Opti("conic") # Tell casadi that it's a conic problem - - # optimization variable: control input - u_var = opti.variable(nu, 1) - - # evaluate at Lie derivative and CBF at the current state - lie_derivative_at_x = self.lie_derivative(X=current_state, u=u_var)['LfV'] - barrier_at_x = self.cbf(X=current_state)['cbf'] - - learned_residual = 0.0 - right_hand_side = 0.0 - - if use_learned_model: - torch_state = torch.from_numpy(current_state) - torch_state = torch.unsqueeze(torch_state, 0) - torch_state = torch_state.to(torch.float32) - a_b = self.mlp(torch_state) - a_b = a_b.detach().numpy() - a = a_b[0, :self.model.nu] - b = a_b[0, -1] - - learned_residual = cs.dot(a.T, u_var) + b - - if self.soft_constrained: - slack_var = opti.variable(1, 1) - - # quadratic objective - cost = 0.5 * cs.norm_2(unsafe_action - u_var) ** 2 + self.slack_weight * slack_var**2 - - # soften CBF constraint - right_hand_side = slack_var - - # Non-negativity constraint on slack variable - opti.subject_to(slack_var >= 0.0) - else: - # quadratic objective - cost = 0.5 * cs.norm_2(unsafe_action - u_var) ** 2 - - # CBF constraint - opti.subject_to(-self.linear_func(x=barrier_at_x)["y"] - lie_derivative_at_x - learned_residual <= right_hand_side) - - # input constraints - for input_constraint in self.input_constraints_sym: - opti.subject_to(input_constraint(u_var) <= 0) - - opti.minimize(cost) - - # set verbosity option of optimizer - - # opts = {'printLevel': 'none'} - # opts = {} - # select QP solver - # opti.solver('qpoases', opts) - if platform == "linux": - opts = {'printLevel': 'low', 'error_on_fail': False} - opti.solver('qpoases', opts) - elif platform == "darwin": - opts = {'error_on_fail': False} - opti.solver('qrqp', opts) - - self.opti_dict = { - "opti": opti, - "u_var": u_var, - "cost": cost - } - - if self.soft_constrained: - self.opti_dict["slack_var"] = slack_var - - success = False - try: - # solve optimization problem - sol = opti.solve() - success = True - - u_val = sol.value(u_var) - if self.soft_constrained: - slack_var = sol.value(slack_var) - if slack_var > self.slack_tolerance: - print("Slack:", slack_var) - success = False - cost = sol.value(cost) - except RuntimeError as e: - print(e) - success = False - u_val = opti.debug.value(u_var) - print("u", u_val) - if self.soft_constrained: - slack_val = opti.debug.value(slack_var) - print("slack", slack_val) - print(self.lie_derivative(X=current_state, u=u_val)['LfV']) - print(self.linear_func(x=barrier_at_x)["y"]) - print("------------------------------------------------") - - return u_val, success - - def select_action(self, current_state, use_learned_model=True): - """Select the action to apply to the system. - - """ - if self.unsafe_controller is not None: - # use underlying (potentially unsafe) control input - unsafe_input = self.unsafe_controller.select_action(current_state) - else: - # create random control input - unsafe_input = 2.0 * (2.0 * np.random.random(size=self.model.nu) - 1.0) - - # create sinusoidal control input - # unsafe_input = 0.5 * np.sin(2 * np.pi / 50 * (self.env.pyb_step_counter // self.step_size) - np.pi) + 0.0 - - # certify control input - safe_input, success = self.certify_action(current_state, unsafe_input, use_learned_model) - - return safe_input, unsafe_input, success - - def compute_loss(self, batch): - """Compute training loss of the neural network that represents the Lie derivative error""" - state, act, barrier_dot, barrier_dot_approx = batch["state"], batch["act"], batch["barrier_dot"], \ - batch["barrier_dot_approx"] - - # predict a and b vectors - a_b = self.mlp(state) - a = torch.unsqueeze(a_b[:, 0], 1) - b = torch.unsqueeze(a_b[:, 1], 1) - - # determine the estimate of h_dot - h_dot_estimate = barrier_dot + a * act + b - - # determine loss - loss = (h_dot_estimate - barrier_dot_approx).pow(2).mean() - return loss - - def update(self, batch): - """Update the neural network parameters. - - """ - loss = self.compute_loss(batch) - self.opt.zero_grad() - loss.backward() - self.opt.step() - - def save(self, path): - """Saves model params and experiment state to checkpoint path. - - """ - path_dir = os.path.dirname(path) - os.makedirs(path_dir, exist_ok=True) - - state_dict = { - "agent": self.mlp.state_dict() - } - if self.training: - exp_state = { - "buffer": self.buffer.state_dict() - } - state_dict.update(exp_state) - torch.save(state_dict, path) - - def load(self, path): - """Restores model and experiment given checkpoint path. - - """ - state = torch.load(path) - - # restore params - self.mlp.load_state_dict(state["agent"]) - - # restore experiment state - if self.training: - self.buffer.load_state_dict(state["buffer"]) - - def learn(self): - """Learn the error in the Lie derivative from multiple experiments. - - """ - input_blending_weight = np.arange(self.num_episodes) / (self.num_episodes - 1) - - # Run experiments in loop - for i in range(self.num_episodes): - # reset the episode - self.reset() - - counter = 0 - - obs = self.env.reset() - - # create arrays to collect data - states = np.zeros((self.max_num_steps, self.model.nx)) - inputs = np.zeros((self.max_num_steps, self.model.nu)) - barrier_values = np.zeros((self.max_num_steps, 1)) - lie_derivative_values = np.zeros((self.max_num_steps, 1)) - lie_derivative_est = np.zeros((self.max_num_steps, 1)) - - self.logger.dump_scalars() - - while counter < self.max_num_steps: - print("Step: ", self.env.pyb_step_counter // self.step_size) - - # determine safe action - safe_action, unsafe_action, success = self.select_action(obs) - - # blend the safe and unsafe action - blended_input = (1 - input_blending_weight[i]) * unsafe_action + input_blending_weight[i] * safe_action - - # Step the system - obs, reward, done, info = self.env.step(blended_input) - print("obs: {}".format(obs)) - print("action: {}\n".format(safe_action)) - - self.logger.add_scalars({ - "safe_input": safe_action, - "unsafe_input": unsafe_action, - "applied_input": blended_input}, - counter, - prefix="action") - self.logger.add_scalars({ - "x_pos": obs[0], - "x_vel": obs[1], - "theta": obs[2], - "theta_dot": obs[3]}, - counter, - prefix="state") - - # collect data - states[counter, :] = obs - inputs[counter, :] = blended_input - barrier_values[counter, :] = self.cbf(X=obs)['cbf'] - lie_derivative_values[counter, :] = self.lie_derivative(X=obs, u=blended_input)['LfV'] - - # Determine the estimated Lie derivative - torch_state = torch.from_numpy(obs) - torch_state = torch.unsqueeze(torch_state, 0) - torch_state = torch_state.to(torch.float32) - a_b = self.mlp(torch_state) - a_b = a_b.detach().numpy() - a = a_b[0, :self.model.nu] - b = a_b[0, -1] - lie_derivative_est[counter, :] = lie_derivative_values[counter, :] + np.dot(a.T, blended_input) + b - - counter += 1 - - print("Num time steps:", len(inputs)) - print("Certified control input weight:", input_blending_weight[i]) - - # numerical time differentiation (symmetric) of barrier function : - barrier_dot_approx = (barrier_values[2:] - barrier_values[:-2]) / (2 * 1 / self.env.CTRL_FREQ) - - # compare actual and numerical time derivatives - # import matplotlib.pyplot as plt - # t = np.arange(self.max_num_steps) / self.env.CTRL_FREQ - # plt.plot(t[1:-1], barrier_dot_approx, "r", label="h_dot_numerical") - # plt.plot(t, lie_derivative_values, "b", label="h_dot_hat") - # plt.plot(t, lie_derivative_est, "g", label="h_dot_est") - # plt.xlabel("t") - # plt.ylabel("h_dot") - # plt.legend() - # plt.show() - - # Add data to buffer - self.buffer.push({ - "state": states[1:-1, :], - "act": inputs[1:-1, :], - "barrier_dot": lie_derivative_values[1:-1, :], - "barrier_dot_approx": barrier_dot_approx - }) - - # Update neural network parameters - for j in range(self.train_iterations): - batch = self.buffer.sample(self.train_batch_size, self.device) - self.update(batch) - - # Save model parameters - print("Saving current model parameters at:", self.checkpoint_path) - self.save(self.checkpoint_path) - - def run(self, render=False, logging=False): - """Runs evaluation with current policy. - - Args: - render (bool): if to render during the runs. - logging (bool): if to log using logger during the runs. - - Returns: - stats_buffer (CBFBuffer): Buffer of the experiment results - - """ - # load model from training episode - self.load(self.checkpoint_path) - - counter = 0 - - obs = self.env.reset() - - self.logger.add_scalars({ - "x_pos": obs[0], - "x_dot": obs[1], - "theta": obs[2], - "theta_dot": obs[3]}, - counter, - prefix="state") - - # while len(ep_returns) < self.eval_batch_size and counter < self.max_num_steps: - while counter < self.max_num_steps: - print("Step: ", self.env.pyb_step_counter // self.step_size) - - safe_action, unsafe_action, success = self.select_action(obs, self.use_learned_model) - - # Check the system's performance without certification - if self.use_safe_input: - action = safe_action - else: - action = unsafe_action - - obs, reward, done, info = self.env.step(action) - print("obs: {}".format(obs)) - print("action: {}\n".format(action)) - - self.logger.add_scalars({ - "safe_input": safe_action, - "unsafe_input": unsafe_action, - "applied_input": action}, - counter, - prefix="action") - self.logger.add_scalars({ - "x_pos": obs[0], - "x_dot": obs[1], - "theta": obs[2], - "theta_dot": obs[3]}, - counter + 1, - prefix="state") - counter += 1 - - return self.logger.stats_buffer diff --git a/safe_control_gym/controllers/cbf/cbf_qp.yaml b/safe_control_gym/controllers/cbf/cbf_qp.yaml deleted file mode 100644 index f7e4ab208..000000000 --- a/safe_control_gym/controllers/cbf/cbf_qp.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# cbf qp args - -#prior_prop: None -#prior_prop: -# pole_length: 0.4 -# pole_mass: 0.12 -# cart_mass: 0.8 - -#initial_state: -# init_x: -1.0 -# init_x_dot: 0.0 -# init_theta: 0.1 -# init_theta_dot: 0.0 - -randomized_init: True - -# unsafe controller -unsafe_controller: "lqr" - -# linear function parameters -#slope: 10.0 -#slope: 1.0 -slope: 0.1 -#slope: 0.01 - -# CBF ellipsoid parameters -#x_pos_max: 1.0 -#x_vel_max: 1.0 -#theta_max: 0.1 -#theta_dot_max: 0.1 - -x_pos_max: 0.4 -x_vel_max: 0.5 -theta_max: 0.1 -theta_dot_max: 0.1 - -# runner args -deque_size: 10 -eval_batch_size: 1 - -# Plot -plot_traj: False -save_plot: True -plot_dir: "./results/temp/" diff --git a/safe_control_gym/controllers/cbf/cbf_qp_learn.yaml b/safe_control_gym/controllers/cbf/cbf_qp_learn.yaml deleted file mode 100644 index d1f4e48b7..000000000 --- a/safe_control_gym/controllers/cbf/cbf_qp_learn.yaml +++ /dev/null @@ -1,61 +0,0 @@ -algo_config: - max_num_steps: 250 - - soft_constrained: True - slack_weight: 10000.0 # 0.0 - slack_tolerance: 1.0E-3 # 0.0 - - hidden_dims: [256, 256] - learning_rate: 0.001 - - num_episodes: 20 - max_buffer_size: 1.0E+6 - train_batch_size: 64 - train_iterations: 200 - - - - -task_config: - ctrl_freq: 100 - pyb_freq: 1000 - - # randomization - inertial_prop: null - randomized_inertial_prop: False - inertial_prop_randomization_info: null - - #prior_prop: null - prior_prop: - pole_length: 0.4 - pole_mass: 0.12 - cart_mass: 0.8 - - randomized_init: True - init_state_randomization_info: - "init_x": - "distrib": "uniform" - "low": -1.0 - "high": 1.0 - "init_x_dot": - "distrib": "uniform" - "low": -0.5 - "high": 0.5 - "init_theta": - "distrib": "uniform" - "low": -0.1 - "high": 0.1 - "init_theta_dot": - "distrib": "uniform" - "low": -0.1 - "high": 0.1 - # constraints - constraints: - - constraint_form: default_constraint - constrained_variable: state - - constraint_form: default_constraint - constrained_variable: input - upper_bounds: - - 8.0 - lower_bounds: - - -8.0 diff --git a/safe_control_gym/controllers/cbf/cbf_qp_test.yaml b/safe_control_gym/controllers/cbf/cbf_qp_test.yaml deleted file mode 100644 index f49842d49..000000000 --- a/safe_control_gym/controllers/cbf/cbf_qp_test.yaml +++ /dev/null @@ -1,57 +0,0 @@ -algo_config: - max_num_steps: 500 - use_safe_input: True - use_learned_model: True - - soft_constrained: True - slack_weight: 10000.0 # 0.0 - slack_tolerance: 1.0E-3 # 0.0 - - hidden_dims: [256, 256] - learning_rate: 0.001 - - num_episodes: 1 - max_buffer_size: 1.0E+6 - train_batch_size: 64 - train_iterations: 200 - - - - - - -task_config: - ctrl_freq: 100 - pyb_freq: 1000 - - # randomization - inertial_prop: null - randomized_inertial_prop: False - inertial_prop_randomization_info: null - - #prior_prop: null - prior_prop: - pole_length: 0.4 - pole_mass: 0.12 - cart_mass: 0.8 - - randomized_init: False - init_state_randomization_info: null - - # state initialization - init_state: - init_x: -0.3 - init_x_dot: 0.0 - init_theta: 0.06 - init_theta_dot: -0.04 - - # constraints - constraints: - - constraint_form: default_constraint - constrained_variable: state - - constraint_form: default_constraint - constrained_variable: input - upper_bounds: - - 8.0 - lower_bounds: - - -8.0 diff --git a/safe_control_gym/controllers/cbf/cbf_qp_utils.py b/safe_control_gym/controllers/cbf/cbf_qp_utils.py deleted file mode 100644 index e5b3382cf..000000000 --- a/safe_control_gym/controllers/cbf/cbf_qp_utils.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Control barrier function (CBF) utilities - -""" -import numpy as np -import casadi as cs -import torch -from gym.spaces import Box - - -def cbf_cartpole(X, x_pos_max, x_vel_max, theta_max, theta_dot_max): - """ - Creates a control barrier function (CBF) candidate based on an ellipsoid function - - Attributes: - X (cs.sym.MX): Symbolic state variable - x_pos_max (float): Semi axis of ellipsoid in the first dimension (not used) - x_vel_max (float): Semi axis of ellipsoid in the second dimension (not used) - theta_max (float): Semi axis of ellipsoid in the third dimension - theta_dot_max (float): Semi axis of ellipsoid in the fourth dimension - - Returns: - cbf_func (cs.Function): Function object of the control barrier function (CBF) - - """ - # cbf = 0.5 * (1 - (X[2]) ** 2 / (theta_max) ** 2 - (X[3]) ** 2 / (theta_dot_max) ** 2) - # cbf_func = cs.Function('barrier', [X], [cbf], ['X'], ['cbf']) - # cbf = 0.5 * (1 - (X[0]) ** 2 / (x_pos_max) ** 2) - # cbf_func = cs.Function('barrier', [X], [cbf], ['X'], ['cbf']) - # cbf = 0.5 * (1 - (X[2]) ** 2 / (theta_max) ** 2) - # cbf_func = cs.Function('barrier', [X], [cbf], ['X'], ['cbf']) - # cbf = 0.5 * (1 - (X[0]) ** 2 / (x_pos_max) ** 2 - (X[3]) ** 2 / (theta_dot_max) ** 2) - # cbf_func = cs.Function('barrier', [X], [cbf], ['X'], ['cbf']) - cbf = 0.5 * (1 - (X[0]) ** 2 / (x_pos_max) ** 2 - (X[1]) ** 2 / (x_vel_max) ** 2) - cbf_func = cs.Function('barrier', [X], [cbf], ['X'], ['cbf']) - return cbf_func - - -def cbf_quadrotor(): - # TODO: Implementation missing - raise NotImplementedError - - -def linear_function(slope): - """ - Creates a one dimensional linear function - - Attributes: - slope (float): The slope of the linear function - - Returns: - linear_func (cs.Function): Function object of a linear function with desired slope - - """ - x = cs.MX.sym("x", 1) - linear_func = cs.Function('linear', [x], [slope * x], ["x"], ["y"]) - return linear_func - - -def cartesian_product(*arrays): - """ - Creates the cartesian product of a list of arrays from: - https://stackoverflow.com/a/11146645 - - Attributes: - arrays (list): List of arrays - - Returns: - Cartesian product of all arrays in the list - - """ - la = len(arrays) - dtype = np.result_type(*arrays) - arr = np.empty([len(a) for a in arrays] + [la], dtype=dtype) - for i, a in enumerate(np.ix_(*arrays)): - arr[..., i] = a - return arr.reshape(-1, la) - - -class CBFBuffer(object): - """Storage for replay buffer during training. - - Attributes: - max_size (int): maximum size of the replay buffer. - batch_size (int): number of samples (steps) per batch. - scheme (dict): describs shape & other info of data to be stored. - keys (list): names of all data from scheme. - - """ - - def __init__(self, obs_space, act_space, max_size, batch_size=None): - super().__init__() - self.max_size = max_size - self.batch_size = batch_size - - obs_dim = obs_space.shape - if isinstance(act_space, Box): - act_dim = act_space.shape[0] - else: - act_dim = act_space.n - - N = max_size - self.scheme = { - "state": { - "vshape": (N, *obs_dim) - }, - "act": { - "vshape": (N, act_dim) - }, - "barrier_dot": { - "vshape": (N, 1) - }, - "barrier_dot_approx": { - "vshape": (N, 1) - } - } - self.keys = list(self.scheme.keys()) - self.reset() - - def reset(self): - """Allocate space for containers. - - """ - for k, info in self.scheme.items(): - assert "vshape" in info, "Scheme must define vshape for {}".format(k) - vshape = info["vshape"] - dtype = info.get("dtype", np.float32) - init = info.get("init", np.zeros) - self.__dict__[k] = init(vshape, dtype=dtype) - - self.pos = 0 - self.buffer_size = 0 - - def __len__(self): - """Returns current size of the buffer. - - """ - return self.buffer_size - - def state_dict(self): - """Returns a snapshot of current buffer. - - """ - state = dict( - pos=self.pos, - buffer_size=self.buffer_size, - ) - for k in self.scheme: - v = self.__dict__[k] - state[k] = v - return state - - def load_state_dict(self, state): - """Restores buffer from previous state. - - """ - for k, v in state.items(): - self.__dict__[k] = v - - def push(self, batch): - """Inserts transition step data (as dict) to storage. - - """ - # batch size - k = list(batch.keys())[0] - n = batch[k].shape[0] - - for k, v in batch.items(): - shape = self.scheme[k]["vshape"][1:] - dtype = self.scheme[k].get("dtype", np.float32) - v_ = np.asarray(v, dtype=dtype).reshape((n,) + shape) - - if self.pos + n <= self.max_size: - self.__dict__[k][self.pos:self.pos + n] = v_ - else: - # wrap around - remain_n = self.pos + n - self.max_size - self.__dict__[k][self.pos:self.max_size] = v_[:-remain_n] - self.__dict__[k][:remain_n] = v_[-remain_n:] - - self.pos = (self.pos + n) % self.max_size - if self.buffer_size < self.max_size: - self.buffer_size = min(self.max_size, self.pos + n) - - def sample(self, batch_size=None, device=None): - """Returns data batch. - - """ - if not batch_size: - batch_size = self.batch_size - - indices = np.random.randint(0, len(self), size=batch_size) - batch = {} - for k, info in self.scheme.items(): - shape = info["vshape"][1:] - v = self.__dict__[k].reshape(-1, *shape)[indices] - if device is None: - batch[k] = torch.as_tensor(v) - else: - batch[k] = torch.as_tensor(v, device=device) - return batch diff --git a/safe_control_gym/controllers/cbf/cbf_qp_verify.yaml b/safe_control_gym/controllers/cbf/cbf_qp_verify.yaml deleted file mode 100644 index a97587244..000000000 --- a/safe_control_gym/controllers/cbf/cbf_qp_verify.yaml +++ /dev/null @@ -1,66 +0,0 @@ -algo_config: - max_num_steps: 1 - use_safe_input: True - use_learned_model: False - - soft_constrained: False - slack_weight: 10000.0 # 0.0 - slack_tolerance: 1.0E-3 # 0.0 - - # Verify CBF parameters - # tolerance: 0.0 - tolerance: 0.05 - # num_points: 10 - num_points: 50 - - hidden_dims: [256, 256] - learning_rate: 0.001 - - num_episodes: 1 - max_buffer_size: 1.0E+6 - train_batch_size: 64 - train_iterations: 200 - - - - -task_config: - ctrl_freq: 100 - pyb_freq: 1000 - - # randomization - inertial_prop: null - randomized_inertial_prop: False - inertial_prop_randomization_info: null - - #prior_prop: null - prior_prop: - pole_length: 0.4 - pole_mass: 0.12 - cart_mass: 0.8 - - randomized_init: False - init_state_randomization_info: null - - # state initialization - init_state: - init_x: -0.3 - init_x_dot: 0.0 - init_theta: 0.06 - init_theta_dot: -0.04 - - # constraints - # constraints: - # cartpole_input: - # low: -8.0 - # high: 8.0 - - constraints: - - constraint_form: default_constraint - constrained_variable: state - - constraint_form: default_constraint - constrained_variable: input - upper_bounds: - - 8.0 - lower_bounds: - - -8.0 diff --git a/safe_control_gym/controllers/ddpg/ddpg.py b/safe_control_gym/controllers/ddpg/ddpg.py index 60c3ed3cc..d0ebbdd4d 100644 --- a/safe_control_gym/controllers/ddpg/ddpg.py +++ b/safe_control_gym/controllers/ddpg/ddpg.py @@ -1,45 +1,40 @@ -"""Deep Deterministic Policy Gradient +'''Deep Deterministic Policy Gradient Reference paper & code: * [Continuous Control with Deep Reinforcement Learning](https://arxiv.org/pdf/1509.02971.pdf) * [openai spinning up - ddpg](https://github.com/openai/spinningup/tree/master/spinup/algos/pytorch/ddpg) * [DeepRL - ddpg](https://github.com/ShangtongZhang/DeepRL/blob/master/deep_rl/agent/DDPG_agent.py) +''' -Example: - $ python experiments/main.py --algo ddpg --task cartpole --output_dir results --tag test/cartpole_ddpg --seed 6 - -Todo - * - -""" import os import time -import copy -import numpy as np -import torch from collections import defaultdict -from safe_control_gym.utils.logging import ExperimentLogger -from safe_control_gym.utils.utils import get_random_state, set_random_state, is_wrapped -from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs -from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer +import numpy as np +import torch from safe_control_gym.controllers.base_controller import BaseController from safe_control_gym.controllers.ddpg.ddpg_utils import DDPGAgent, DDPGBuffer, make_action_noise_process +from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics, + VecRecordEpisodeStatistics) +from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs +from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs +from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer, + RewardStdNormalizer) +from safe_control_gym.utils.logging import ExperimentLogger +from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state class DDPG(BaseController): - """deep deterministic policy gradient.""" - - def __init__(self, - env_func, - training=True, - checkpoint_path="model_latest.pt", - output_dir="temp", - use_gpu=False, - seed=0, + '''Deep Deterministic Policy Gradient.''' + + def __init__(self, + env_func, + training=True, + checkpoint_path='model_latest.pt', + output_dir='temp', + use_gpu=False, + seed=0, **kwargs): super().__init__(env_func, training, checkpoint_path, output_dir, use_gpu, seed, **kwargs) @@ -61,12 +56,12 @@ def __init__(self, # agent self.agent = DDPGAgent(self.env.observation_space, - self.env.action_space, - hidden_dim=self.hidden_dim, - gamma=self.gamma, - tau=self.tau, - actor_lr=self.actor_lr, - critic_lr=self.critic_lr) + self.env.action_space, + hidden_dim=self.hidden_dim, + gamma=self.gamma, + tau=self.tau, + actor_lr=self.actor_lr, + critic_lr=self.critic_lr) self.agent.to(self.device) # pre-/post-processing @@ -89,84 +84,84 @@ def __init__(self, self.logger = ExperimentLogger(output_dir, log_file_out=log_file_out, use_tensorboard=use_tensorboard) def reset(self): - """Prepares for training or testing.""" + '''Prepares for training or testing.''' if self.training: # set up stats tracking - self.env.add_tracker("constraint_violation", 0) - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0) + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('mse', 0, mode='queue') self.total_steps = 0 obs, _ = self.env.reset() self.obs = self.obs_normalizer(obs) self.buffer = DDPGBuffer(self.env.observation_space, self.env.action_space, self.max_buffer_size, self.train_batch_size) - # reset/initial noise process + # reset/initial noise process if self.noise_process: self.noise_process.reset_states() else: # set up stats tracking - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.env.add_tracker("constraint_values", 0, mode="queue") - self.env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.env.add_tracker('constraint_values', 0, mode='queue') + self.env.add_tracker('mse', 0, mode='queue') def close(self): - """Shuts down and cleans up lingering resources.""" + '''Shuts down and cleans up lingering resources.''' self.env.close() if self.training: self.eval_env.close() self.logger.close() def save(self, path, save_buffer=True): - """Saves model params and experiment state to checkpoint path.""" + '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) os.makedirs(path_dir, exist_ok=True) state_dict = { - "agent": self.agent.state_dict(), - "obs_normalizer": self.obs_normalizer.state_dict(), - "reward_normalizer": self.reward_normalizer.state_dict() + 'agent': self.agent.state_dict(), + 'obs_normalizer': self.obs_normalizer.state_dict(), + 'reward_normalizer': self.reward_normalizer.state_dict() } if self.training: exp_state = { - "total_steps": self.total_steps, - "obs": self.obs, - "random_state": get_random_state(), - "env_random_state": self.env.get_env_random_state() + 'total_steps': self.total_steps, + 'obs': self.obs, + 'random_state': get_random_state(), + 'env_random_state': self.env.get_env_random_state() } # latest checkpoint shoud enable save_buffer (for experiment restore), # but intermediate checkpoint shoud not, to save storage (buffer is large) if save_buffer: - exp_state["buffer"] = self.buffer.state_dict() - # noise process is also stateful + exp_state['buffer'] = self.buffer.state_dict() + # noise process is also stateful if self.noise_process: - exp_state["noise_process"] = self.noise_process.state_dict() + exp_state['noise_process'] = self.noise_process.state_dict() state_dict.update(exp_state) torch.save(state_dict, path) def load(self, path): - """Restores model and experiment given checkpoint path.""" + '''Restores model and experiment given checkpoint path.''' state = torch.load(path) # restore params - self.agent.load_state_dict(state["agent"]) - self.obs_normalizer.load_state_dict(state["obs_normalizer"]) - self.reward_normalizer.load_state_dict(state["reward_normalizer"]) + self.agent.load_state_dict(state['agent']) + self.obs_normalizer.load_state_dict(state['obs_normalizer']) + self.reward_normalizer.load_state_dict(state['reward_normalizer']) # restore experiment state if self.training: - self.total_steps = state["total_steps"] - self.obs = state["obs"] - set_random_state(state["random_state"]) - self.env.set_env_random_state(state["env_random_state"]) - if "buffer" in state: - self.buffer.load_state_dict(state["buffer"]) + self.total_steps = state['total_steps'] + self.obs = state['obs'] + set_random_state(state['random_state']) + self.env.set_env_random_state(state['env_random_state']) + if 'buffer' in state: + self.buffer.load_state_dict(state['buffer']) if self.noise_process: - self.noise_process.load_state_dict(state["noise_process"]) + self.noise_process.load_state_dict(state['noise_process']) self.logger.load(self.total_steps) def learn(self, env=None, **kwargs): - """Performs learning (pre-training, training, fine-tuning, etc).""" + '''Performs learning (pre-training, training, fine-tuning, etc).''' while self.total_steps < self.max_env_steps: results = self.train_step() @@ -174,33 +169,50 @@ def learn(self, env=None, **kwargs): if self.total_steps >= self.max_env_steps or (self.save_interval and self.total_steps % self.save_interval == 0): # latest/final checkpoint self.save(self.checkpoint_path) - self.logger.info("Checkpoint | {}".format(self.checkpoint_path)) + self.logger.info(f'Checkpoint | {self.checkpoint_path}') if self.num_checkpoints and self.total_steps % (self.max_env_steps // self.num_checkpoints) == 0: # intermediate checkpoint - path = os.path.join(self.output_dir, "checkpoints", "model_{}.pt".format(self.total_steps)) + path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') self.save(path, save_buffer=False) # eval if self.eval_interval and self.total_steps % self.eval_interval == 0: eval_results = self.run(env=self.eval_env, n_episodes=self.eval_batch_size) - results["eval"] = eval_results - self.logger.info("Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}".format(eval_results["ep_lengths"].mean(), - eval_results["ep_lengths"].std(), - eval_results["ep_returns"].mean(), - eval_results["ep_returns"].std())) + results['eval'] = eval_results + self.logger.info('Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}'.format(eval_results['ep_lengths'].mean(), + eval_results['ep_lengths'].std(), + eval_results['ep_returns'].mean(), + eval_results['ep_returns'].std())) # save best model - eval_score = eval_results["ep_returns"].mean() - eval_best_score = getattr(self, "eval_best_score", -np.infty) + eval_score = eval_results['ep_returns'].mean() + eval_best_score = getattr(self, 'eval_best_score', -np.infty) if self.eval_save_best and eval_best_score < eval_score: self.eval_best_score = eval_score - self.save(os.path.join(self.output_dir, "model_best.pt")) + self.save(os.path.join(self.output_dir, 'model_best.pt')) # logging if self.log_interval and self.total_steps % self.log_interval == 0: self.log_step(results) + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. + + Args: + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. + + Returns: + action (ndarray): The action chosen by the controller. + ''' + + with torch.no_grad(): + obs = torch.FloatTensor(obs).to(self.device) + action = self.agent.ac.act(obs) + + return action + def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): - """Runs evaluation with current policy.""" + '''Runs evaluation with current policy.''' self.agent.eval() self.obs_normalizer.set_read_only() if env is None: @@ -209,9 +221,9 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): if not is_wrapped(env, RecordEpisodeStatistics): env = RecordEpisodeStatistics(env, n_episodes) # Add episodic stats to be tracked. - env.add_tracker("constraint_violation", 0, mode="queue") - env.add_tracker("constraint_values", 0, mode="queue") - env.add_tracker("mse", 0, mode="queue") + env.add_tracker('constraint_violation', 0, mode='queue') + env.add_tracker('constraint_values', 0, mode='queue') + env.add_tracker('mse', 0, mode='queue') obs, info = env.reset() obs = self.obs_normalizer(obs) @@ -219,30 +231,28 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): frames = [] while len(ep_returns) < n_episodes: - with torch.no_grad(): - obs = torch.FloatTensor(obs).to(self.device) - action = self.agent.ac.act(obs) + action = self.select_action(obs, info) - obs, reward, done, info = env.step(action) + obs, _, done, info = env.step(action) if render: env.render() - frames.append(env.render("rgb_array")) + frames.append(env.render('rgb_array')) if verbose: - print("obs {} | act {}".format(obs, action)) + print(f'obs {obs} | act {action}') if done: - assert "episode" in info - ep_returns.append(info["episode"]["r"]) - ep_lengths.append(info["episode"]["l"]) + assert 'episode' in info + ep_returns.append(info['episode']['r']) + ep_lengths.append(info['episode']['l']) obs, info = env.reset() obs = self.obs_normalizer(obs) # collect evaluation results ep_lengths = np.asarray(ep_lengths) ep_returns = np.asarray(ep_returns) - eval_results = {"ep_returns": ep_returns, "ep_lengths": ep_lengths} + eval_results = {'ep_returns': ep_returns, 'ep_lengths': ep_lengths} if len(frames) > 0: - eval_results["frames"] = frames + eval_results['frames'] = frames # Other episodic stats from evaluation env. if len(env.queued_stats) > 0: queued_stats = {k: np.asarray(v) for k, v in env.queued_stats.items()} @@ -250,7 +260,7 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): return eval_results def train_step(self, **kwargs): - """Performs a training step.""" + '''Performs a training step.''' self.agent.train() self.obs_normalizer.unset_read_only() obs = self.obs @@ -273,13 +283,13 @@ def train_step(self, **kwargs): # time truncation is not true termination terminal_idx, terminal_obs = [], [] - for idx, inf in enumerate(info["n"]): - if "terminal_info" not in inf: + for idx, inf in enumerate(info['n']): + if 'terminal_info' not in inf: continue - inff = inf["terminal_info"] - if "TimeLimit.truncated" in inff and inff["TimeLimit.truncated"]: + inff = inf['terminal_info'] + if 'TimeLimit.truncated' in inff and inff['TimeLimit.truncated']: terminal_idx.append(idx) - terminal_obs.append(inf["terminal_observation"]) + terminal_obs.append(inf['terminal_observation']) if len(terminal_obs) > 0: terminal_obs = _unflatten_obs(self.obs_normalizer(_flatten_obs(terminal_obs))) @@ -292,13 +302,13 @@ def train_step(self, **kwargs): true_next_obs = _flatten_obs(true_next_obs) self.buffer.push({ - "obs": obs, - "act": act, - "rew": rew, - # "next_obs": next_obs, - # "mask": mask, - "next_obs": true_next_obs, - "mask": true_mask, + 'obs': obs, + 'act': act, + 'rew': rew, + # 'next_obs': next_obs, + # 'mask': mask, + 'next_obs': true_next_obs, + 'mask': true_mask, }) obs = next_obs @@ -318,67 +328,67 @@ def train_step(self, **kwargs): results[k].append(v) results = {k: sum(v) / len(v) for k, v in results.items()} - results.update({"step": self.total_steps, "elapsed_time": time.time() - start}) + results.update({'step': self.total_steps, 'elapsed_time': time.time() - start}) return results def log_step(self, results): - """Does logging after a training step.""" - step = results["step"] + '''Does logging after a training step.''' + step = results['step'] # runner stats self.logger.add_scalars( { - "step": step, - "time": results["elapsed_time"], - "progress": step / self.max_env_steps, + 'step': step, + 'time': results['elapsed_time'], + 'progress': step / self.max_env_steps, }, step, - prefix="time", + prefix='time', write=False, write_tb=False) # learning stats - if "policy_loss" in results: + if 'policy_loss' in results: self.logger.add_scalars( { - k: results[k] - for k in ["policy_loss", "critic_loss"] - }, - step, - prefix="loss") + k: results[k] + for k in ['policy_loss', 'critic_loss'] + }, + step, + prefix='loss') # performance stats ep_lengths = np.asarray(self.env.length_queue) ep_returns = np.asarray(self.env.return_queue) - ep_constraint_violation = np.asarray(self.env.queued_stats["constraint_violation"]) + ep_constraint_violation = np.asarray(self.env.queued_stats['constraint_violation']) self.logger.add_scalars( { - "ep_length": ep_lengths.mean(), - "ep_return": ep_returns.mean(), - "ep_reward": (ep_returns / ep_lengths).mean(), - "ep_constraint_violation": ep_constraint_violation.mean() + 'ep_length': ep_lengths.mean(), + 'ep_return': ep_returns.mean(), + 'ep_reward': (ep_returns / ep_lengths).mean(), + 'ep_constraint_violation': ep_constraint_violation.mean() }, step, - prefix="stat") + prefix='stat') # total constraint violation during learning - total_violations = self.env.accumulated_stats["constraint_violation"] - self.logger.add_scalars({"constraint_violation": total_violations}, step, prefix="stat") - - if "eval" in results: - eval_ep_lengths = results["eval"]["ep_lengths"] - eval_ep_returns = results["eval"]["ep_returns"] - eval_constraint_violation = results["eval"]["constraint_violation"] - eval_mse = results["eval"]["mse"] + total_violations = self.env.accumulated_stats['constraint_violation'] + self.logger.add_scalars({'constraint_violation': total_violations}, step, prefix='stat') + + if 'eval' in results: + eval_ep_lengths = results['eval']['ep_lengths'] + eval_ep_returns = results['eval']['ep_returns'] + eval_constraint_violation = results['eval']['constraint_violation'] + eval_mse = results['eval']['mse'] self.logger.add_scalars( { - "ep_length": eval_ep_lengths.mean(), - "ep_return": eval_ep_returns.mean(), - "ep_reward": (eval_ep_returns / eval_ep_lengths).mean(), - "constraint_violation": eval_constraint_violation.mean(), - "mse": eval_mse.mean() + 'ep_length': eval_ep_lengths.mean(), + 'ep_return': eval_ep_returns.mean(), + 'ep_reward': (eval_ep_returns / eval_ep_lengths).mean(), + 'constraint_violation': eval_constraint_violation.mean(), + 'mse': eval_mse.mean() }, step, - prefix="stat_eval") + prefix='stat_eval') # print summary table self.logger.dump_scalars() diff --git a/safe_control_gym/controllers/ddpg/ddpg_utils.py b/safe_control_gym/controllers/ddpg/ddpg_utils.py index f6b0005b0..eaed162df 100644 --- a/safe_control_gym/controllers/ddpg/ddpg_utils.py +++ b/safe_control_gym/controllers/ddpg/ddpg_utils.py @@ -1,25 +1,20 @@ from collections import defaultdict from copy import deepcopy + import numpy as np import torch import torch.nn as nn -import torch.nn.functional as F -from gym.spaces import Box, Discrete - -from safe_control_gym.math_and_models.distributions import Normal, Categorical -from safe_control_gym.math_and_models.neural_networks import MLP, CNN, RNN, init_ -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer -from safe_control_gym.math_and_models.schedule import * -from safe_control_gym.math_and_models.random_processes import * -from safe_control_gym.controllers.sac.sac_utils import SACBuffer, soft_update +from safe_control_gym.controllers.sac.sac_utils import SACBuffer, soft_update +from safe_control_gym.math_and_models.neural_networks import MLP # ----------------------------------------------------------------------------------- # Agent # ----------------------------------------------------------------------------------- + class DDPGAgent: - """A DDPG class that encapsulates model, optimizer and update functions.""" + '''A DDPG class that encapsulates model, optimizer and update functions.''' def __init__(self, obs_space, @@ -38,7 +33,7 @@ def __init__(self, self.tau = tau # model - self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, activation="relu") + self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, activation='relu') # target networks self.ac_targ = deepcopy(self.ac) @@ -50,45 +45,45 @@ def __init__(self, self.critic_opt = torch.optim.Adam(self.ac.q.parameters(), critic_lr) def to(self, device): - """Puts agent to device.""" + '''Puts agent to device.''' self.ac.to(device) self.ac_targ.to(device) def train(self): - """Sets training mode.""" + '''Sets training mode.''' self.ac.train() def eval(self): - """Sets evaluation mode.""" + '''Sets evaluation mode.''' self.ac.eval() def state_dict(self): - """Snapshots agent state.""" + '''Snapshots agent state.''' return { - "ac": self.ac.state_dict(), - "ac_targ": self.ac_targ.state_dict(), - "actor_opt": self.actor_opt.state_dict(), - "critic_opt": self.critic_opt.state_dict(), + 'ac': self.ac.state_dict(), + 'ac_targ': self.ac_targ.state_dict(), + 'actor_opt': self.actor_opt.state_dict(), + 'critic_opt': self.critic_opt.state_dict(), } def load_state_dict(self, state_dict): - """Restores agent state.""" - self.ac.load_state_dict(state_dict["ac"]) - self.ac_targ.load_state_dict(state_dict["ac_targ"]) - self.actor_opt.load_state_dict(state_dict["actor_opt"]) - self.critic_opt.load_state_dict(state_dict["critic_opt"]) + '''Restores agent state.''' + self.ac.load_state_dict(state_dict['ac']) + self.ac_targ.load_state_dict(state_dict['ac_targ']) + self.actor_opt.load_state_dict(state_dict['actor_opt']) + self.critic_opt.load_state_dict(state_dict['critic_opt']) def compute_policy_loss(self, batch): - """Returns policy loss(es) given batch of data.""" - obs = batch["obs"] + '''Returns policy loss(es) given batch of data.''' + obs = batch['obs'] act = self.ac.actor(obs) q = self.ac.q(obs, act) policy_loss = -q.mean() return policy_loss def compute_q_loss(self, batch): - """Returns q-value loss(es) given batch of data.""" - obs, act, rew, next_obs, mask = batch["obs"], batch["act"], batch["rew"], batch["next_obs"], batch["mask"] + '''Returns q-value loss(es) given batch of data.''' + obs, act, rew, next_obs, mask = batch['obs'], batch['act'], batch['rew'], batch['next_obs'], batch['mask'] q = self.ac.q(obs, act) with torch.no_grad(): @@ -101,8 +96,8 @@ def compute_q_loss(self, batch): return critic_loss def update(self, batch): - """Updates model parameters based on current training batch.""" - resutls = defaultdict(list) + '''Updates model parameters based on current training batch.''' + results = defaultdict(list) # actor update policy_loss = self.compute_policy_loss(batch) @@ -119,9 +114,9 @@ def update(self, batch): # update target networks soft_update(self.ac, self.ac_targ, self.tau) - resutls["policy_loss"] = policy_loss.item() - resutls["critic_loss"] = critic_loss.item() - return resutls + results['policy_loss'] = policy_loss.item() + results['critic_loss'] = critic_loss.item() + return results # ----------------------------------------------------------------------------------- @@ -154,9 +149,9 @@ def forward(self, obs, act): class MLPActorCritic(nn.Module): - """Model for the actor-critic agent.""" + '''Model for the actor-critic agent.''' - def __init__(self, obs_space, act_space, hidden_dims=(64, 64), activation="relu"): + def __init__(self, obs_space, act_space, hidden_dims=(64, 64), activation='relu'): super().__init__() obs_dim = obs_space.shape[0] act_dim = act_space.shape[0] @@ -165,8 +160,10 @@ def __init__(self, obs_space, act_space, hidden_dims=(64, 64), activation="relu" low, high = act_space.low, act_space.high low = torch.FloatTensor(low) high = torch.FloatTensor(high) - # Rescale action from [-1, 1] to [low, high] - unscale_fn = lambda x: low.to(x.device) + (0.5 * (x + 1.0) * (high.to(x.device) - low.to(x.device))) + + def unscale_fn(x): # Rescale action from [-1, 1] to [low, high] + return low.to(x.device) + (0.5 * (x + 1.0) * (high.to(x.device) - low.to(x.device))) + self.actor = MLPActor(obs_dim, act_dim, hidden_dims, activation, postprocess_fn=unscale_fn) # Q functions @@ -182,14 +179,14 @@ def act(self, obs, **kwargs): # ----------------------------------------------------------------------------------- class DDPGBuffer(SACBuffer): - """Storage for replay buffer during training. + '''Storage for replay buffer during training. Attributes: max_size (int): maximum size of the replay buffer. batch_size (int): number of samples (steps) per batch. scheme (dict): describs shape & other info of data to be stored. keys (list): names of all data from scheme. - """ + ''' def __init__(self, obs_space, act_space, max_size, batch_size=None): self.max_size = max_size @@ -200,21 +197,21 @@ def __init__(self, obs_space, act_space, max_size, batch_size=None): N = max_size self.scheme = { - "obs": { - "vshape": (N, *obs_dim) + 'obs': { + 'vshape': (N, *obs_dim) }, - "next_obs": { - "vshape": (N, *obs_dim) + 'next_obs': { + 'vshape': (N, *obs_dim) }, - "act": { - "vshape": (N, act_dim) + 'act': { + 'vshape': (N, act_dim) }, - "rew": { - "vshape": (N, 1) + 'rew': { + 'vshape': (N, 1) }, - "mask": { - "vshape": (N, 1), - "init": np.ones + 'mask': { + 'vshape': (N, 1), + 'init': np.ones } } self.keys = list(self.scheme.keys()) @@ -226,14 +223,13 @@ def __init__(self, obs_space, act_space, max_size, batch_size=None): # ----------------------------------------------------------------------------------- def make_action_noise_process(noise_config, act_space): - """Construct a process for generating action noise during agent training. - """ - process_func = noise_config.pop("func") - std_config = noise_config.pop("std") + '''Construct a process for generating action noise during agent training.''' + process_func = noise_config.pop('func') + std_config = noise_config.pop('std') - std_func = std_config.pop("func") - std_args = std_config.pop("args") + std_func = std_config.pop('func') + std_args = std_config.pop('args') std = eval(std_func)(*std_args, **std_config) - process = eval(process_func)(size=(act_space.shape[0],), std=std) + process = eval(process_func)(size=(act_space.shape[0],), std=std) return process diff --git a/safe_control_gym/controllers/lqr/ilqr.py b/safe_control_gym/controllers/lqr/ilqr.py index bd9db6ace..e6108c259 100644 --- a/safe_control_gym/controllers/lqr/ilqr.py +++ b/safe_control_gym/controllers/lqr/ilqr.py @@ -1,172 +1,93 @@ -"""Linear Quadratic Regulator (LQR) +'''Linear Quadratic Regulator (LQR) [1] https://studywolf.wordpress.com/2016/02/03/the-iterative-linear-quadratic-regulator-method/ [2] https://arxiv.org/pdf/1708.09342.pdf +''' -Example: - - run ilqr on cartpole balance: - - python3 experiments/main.py --func test --tag ilqr_pendulum --algo ilqr --task cartpole - - run ilqr on quadrotor stabilization: - - python3 experiments/main.py --func test --tag ilqr_quad --algo ilqr --task quadrotor --q_lqr 0.1 - - Add the '--render' flag to produce a gif of the results - -""" -import os import numpy as np from termcolor import colored -from matplotlib.ticker import FormatStrFormatter -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.utils.logging import ExperimentLogger from safe_control_gym.controllers.base_controller import BaseController -from safe_control_gym.controllers.lqr.lqr_utils import * -from safe_control_gym.envs.benchmark_env import Cost, Task +from safe_control_gym.controllers.lqr.lqr_utils import (compute_lqr_gain, discretize_linear_system, + get_cost_weight_matrix) +from safe_control_gym.envs.benchmark_env import Task class iLQR(BaseController): - """Linear quadratic regulator. - - Attributes: - env (gym.Env): environment for the task. - Q, R (np.array): cost weight matrix. - x_0, u_0 (np.array): equilibrium state & input. - gain (np.array): input gain matrix. - - """ + '''Linear quadratic regulator.''' def __init__( self, env_func, - # model args - q_lqr=[1], - r_lqr=[1], - discrete_dynamics=1, - # runner args - deque_size=10, - eval_batch_size=1, - # Task - task: Task = Task.STABILIZATION, - task_info=None, - episode_len_sec=10, + # Model args + q_lqr: list = None, + r_lqr: list = None, + discrete_dynamics: bool = True, # iLQR args - max_iterations=15, - lamb_factor=10, - lamb_max=1000, - epsilon=0.01, - # shared/base args - output_dir="./results/temp/", - verbose=True, - random_init=True, - ctrl_freq=240, - pyb_freq=240, - save_data=False, - data_dir=None, - plot_traj=False, - plot_dir=None, - save_plot=False, + max_iterations: int = 15, + lamb_factor: float = 10, + lamb_max: float = 1000, + epsilon: float = 0.01, **kwargs): - """Example of docstring on the __init__ method. - - The __init__ method may be documented in either the class level - docstring, or as a docstring on the __init__ method itself. + '''Creates task and controller. Args: - param1 (str): Description of `param1`. - param2 (:obj:`int`, optional): Description of `param2`. Multiple - lines are supported. - param3 (:obj:`list` of :obj:`str`): Description of `param3`. - - """ - # All params/args (lazy hack). - for k, v in locals().items(): - if k != "self" and k != "kwargs" and "__" not in k: - self.__dict__[k] = v - - # Task. - self.env_func = env_func - self.ctrl_freq = ctrl_freq - self.pyb_freq = pyb_freq - self.deque_size = deque_size - self.task = Task(task) - self.task_info = task_info - self.episode_len_sec = episode_len_sec + env_func (Callable): Function to instantiate task/environment. + q_lqr (list): Diagonals of state cost weight. + r_lqr (list): Diagonals of input/action cost weight. + discrete_dynamics (bool): If to use discrete or continuous dynamics. + max_iterations (int): The number of iterations to train iLQR. + lamb_factor (float): The amount for which to increase lambda when training fails. + lamb_max (float): The maximum lambda allowed. + epsilon (float): The convergence tolerance. + ''' + + super().__init__(env_func, **kwargs) + + # Model parameters + self.q_lqr = q_lqr + self.r_lqr = r_lqr self.discrete_dynamics = discrete_dynamics - # iLQR iterations. + # iLQR parameters self.max_iterations = max_iterations + self.lamb_factor = lamb_factor + self.lamb_max = lamb_max + self.epsilon = epsilon - # iLQR policy update parameters. See [1] for details. - self.lamb_factor = lamb_factor # Factor for scaling lambda - self.lamb_max = lamb_max # Maximum lambda - self.epsilon = epsilon # Tolerance for convergence - - # Stop iteration (to make sure that subsequent iteration number not - # exceeding the first one) - self.stop_iteration = False - - # Plot trajectory. - self.plot_traj = plot_traj - - # Randomize initial state - self.random_init = random_init + self.env = env_func(info_in_reset=True, done_on_out_of_bound=True) - # Plot trajectory. - self.plot_traj = plot_traj - - # Save plot. - self.save_plot = save_plot - - # Plot output directory. - self.plot_dir = plot_dir - if not os.path.exists(plot_dir): - os.makedirs(plot_dir) + # Controller params. + self.model = self.get_prior(self.env) + self.Q = get_cost_weight_matrix(self.q_lqr, self.model.nx) + self.R = get_cost_weight_matrix(self.r_lqr, self.model.nu) + self.env.set_cost_function_param(self.Q, self.R) - # Save data. - self.save_data = save_data + self.gain = compute_lqr_gain(self.model, self.model.X_EQ, self.model.U_EQ, + self.Q, self.R, self.discrete_dynamics) - # Data output directory. - self.data_dir = data_dir - if not os.path.exists(data_dir): - os.makedirs(data_dir) + # Control stepsize. + self.stepsize = self.model.dt - # Logging. - self.logger = ExperimentLogger(output_dir) + self.ite_counter = 0 + self.input_ff_best = None + self.gains_fb_best = None - # Verbose. - self.verbose = verbose + self.reset() def close(self): - """Cleans up resources. - - """ + '''Cleans up resources.''' self.env.close() - self.logger.close() - def run_ilqr(self, render=False, logging=False): - """Run iLQR to iteratively update policy for each time step k + def learn(self, env=None, **kwargs): + '''Run iLQR to iteratively update policy for each time step. Args: - render (bool): Flag to save frames for visualization. - logging (bool): Flag to log results. - - Returns: - ilqr_eval_results (dict): Dictionary containing the results from - each iLQR iteration. - """ - # Snapshot env state - # state_dict = self.env.state_dict() + env (BenchmarkEnv): The environment to be used for training. + ''' - # Initialize iteration logging variables. - ite_returns, ite_lengths, ite_data, frames = [], [], {}, [] - - # Initialize iteration and step counter. - self.ite_counter = 0 - self.k = 0 + if env is None: + env = self.env # Initialize step size self.lamb = 1.0 @@ -174,239 +95,119 @@ def run_ilqr(self, render=False, logging=False): # Set update unstable flag to False self.update_unstable = False - # Initialize list of frames for each iteration - frames_k = [] + # Initialize previous cost + self.previous_total_cost = -float('inf') # Loop through iLQR iterations while self.ite_counter < self.max_iterations: - - # Current goal. - if self.task == Task.STABILIZATION: - current_goal = self.x_0 - elif self.task == Task.TRAJ_TRACKING: - current_goal = self.x_0[self.k] - - # Compute input. - action = self.select_action(self.env.state, self.k) - - # Save rollout data. - if self.k == 0: - # Initialize state and input stack. - state_stack = self.env.state - input_stack = action - goal_stack = current_goal - - # Print initial state. - print(colored("initial state: " + get_arr_str(self.env.state), "green")) - - if self.ite_counter == 0: - self.init_state = self.env.state - else: - # Save state and input. - state_stack = np.vstack((state_stack, self.env.state)) - input_stack = np.vstack((input_stack, action)) - goal_stack = np.vstack((goal_stack, current_goal)) - - # Step forward. - obs, reward, done, info = self.env.step(action) - - # Update step counter. - self.k += 1 - # print("step", k, "done", done) - - # Print out. - if self.verbose and self.k % 100 == 0: - print(colored("episode: %d step: %d" % (self.ite_counter, self.k), "green")) - print("state: " + get_arr_str(self.env.state)) - print("action: " + get_arr_str(self.env.state) + "\n") - - # Save frame for visualization. - if render: - self.env.render() - frames_k.append(self.env.render("rgb_array")) + self.run(env=env, training=True) # Save data and update policy if iteration is finished. - if done: - # Push last state and input to stack. - # Last input is not really used. - state_stack = np.vstack((state_stack, self.env.state)) - # input_stack = np.vstack((input_stack, action)) - # goal_stack = np.vstack((goal_stack, current_goal)) - - # Add set of k frames to frames (for all episodes) - frames.append(frames_k) - frames_k = [] - - # Update iteration return and length lists. - assert "episode" in info - ite_returns.append(info["episode"]["r"]) - ite_lengths.append(info["episode"]["l"]) - ite_data["ite%d_state" % self.ite_counter] = state_stack - ite_data["ite%d_input" % self.ite_counter] = input_stack - - # Print iteration reward. - print(colored("final state: " + get_arr_str(self.env.state), "green")) - print(colored("iteration %d reward %.4f" % - (self.ite_counter, info["episode"]["r"]), "green")) - print(colored("--------------------------", "green")) - - # Break if the first iteration is not successful - if self.task == Task.STABILIZATION: - if self.ite_counter == 0 and not info["goal_reached"]: - print(colored("The initial policy might be unstable. " - + "Break from iLQR updates.", "red")) - break - - # Maximum episode length. - self.num_steps = np.shape(input_stack)[0] - self.episode_len_sec = self.num_steps * self.stepsize - print(colored("Maximum episode length: %d steps!" % (self.num_steps), "blue")) - print(np.shape(input_stack), np.shape(self.gains_fb)) - # import ipdb; ipdb.set_trace() - - # Check if cost is increased and update lambda correspondingly - delta_reward = np.diff(ite_returns[-2:]) - if self.ite_counter == 0: - - # Save best iteration. - print("Save iteration gains. Best iteration %d" % self.ite_counter) - self.best_iteration = self.ite_counter - self.input_ff_best = np.copy(self.input_ff) - self.gains_fb_best = np.copy(self.gains_fb) - - # Update controller gains - self.update_policy(state_stack, input_stack) - - # Initialize improved flag. - self.prev_ite_improved = False - - elif delta_reward < 0.0 or self.update_unstable: - # If cost is increased, increase lambda - self.lamb *= self.lamb_factor - - # Reset feedforward term and controller gain to that from - # the previous iteration. - print("Cost increased by %.2f. " % -delta_reward - + "Set feedforward term and controller gain to that " - "from the previous iteration. " - "Increased lambda to %.2f." % self.lamb) - print("Current policy is from iteration %d." % self.best_iteration) - self.input_ff = np.copy(self.input_ff_best) - self.gains_fb = np.copy(self.gains_fb_best) - - # Set improved flag to False. - self.prev_ite_improved = False - - # Break if maximum lambda is reached. - if self.lamb > self.lamb_max: - print(colored("Maximum lambda reached.", "red")) - self.lamb = self.lamb_max - - # Reset update_unstable flag to False. - self.update_unstable = False - - elif delta_reward >= 0.0: - # If cost is reduced, reduce lambda. - # Smoother convergence if not scaling down lambda. - # self.lamb /= self.lamb_factor - - # Save feedforward term and gain and state and input stacks. - print("Save iteration gains. Best iteration %d" % self.ite_counter) - self.best_iteration = self.ite_counter - self.input_ff_best = np.copy(self.input_ff) - self.gains_fb_best = np.copy(self.gains_fb) - - # Check consecutive reward increment (cost decrement). - if delta_reward < self.epsilon and self.prev_ite_improved: - # Cost converged. - print(colored("iLQR cost converged with a tolerance " - + "of %.2f." % self.epsilon, "yellow")) - break - - # Set improved flag to True. - self.prev_ite_improved = True - - # Update controller gains - self.update_policy(state_stack, input_stack) - - # Reset iteration and step counter. - self.ite_counter += 1 - self.k = 0 - - # Reset environment. - print("Reset environment.") - self.reset_env() - - # Post analysis. - if self.plot_traj or self.save_plot or self.save_data: - analysis_data = post_analysis(goal_stack, state_stack, - input_stack, self.env, 0, - self.ep_counter, - self.plot_traj, - self.save_plot, - self.save_data, - self.plot_dir, self.data_dir) - - # Collect evaluation results. - ite_lengths = np.asarray(ite_lengths) - ite_returns = np.asarray(ite_returns) - if logging: - msg = "****** Evaluation ******\n" - msg += "eval_ep_length {:.2f} +/- {:.2f} | " + \ - "eval_ep_return {:.3f} +/- {:.3f}\n".format( - ite_lengths.mean(), ite_lengths.std(), ite_returns.mean(), - ite_returns.std()) - self.logger.info(msg + "\n") - - ilqr_eval_results = { - "ite_returns": ite_returns, - "ite_lengths": ite_lengths, - "ite_data": ite_data - } - - if len(frames) > 0: - ilqr_eval_results["frames"] = frames - - return ilqr_eval_results - - def update_policy(self, state_stack, input_stack): - """One-line description. + self.state_stack = np.vstack((self.state_stack, self.final_obs)) + + print(colored(f'Iteration: {self.ite_counter}, Cost: {self.total_cost}', 'green')) + print(colored('--------------------------', 'green')) + + if self.ite_counter == 0 and env.done_on_out_of_bound and self.final_info['out_of_bounds']: + print(colored('[ERROR] The initial policy might be unstable. Break from iLQR updates.', 'red')) + break + + # Maximum episode length. + self.num_steps = np.shape(self.input_stack)[0] + + # Check if cost is increased and update lambda correspondingly + delta_cost = self.total_cost - self.previous_total_cost + if self.ite_counter == 0: + # Save best iteration. + self.best_iteration = self.ite_counter + self.previous_total_cost = self.total_cost + self.input_ff_best = np.copy(self.input_ff) + self.gains_fb_best = np.copy(self.gains_fb) + + # Update controller gains + self.update_policy(env) + + # Initialize improved flag. + self.prev_ite_improved = False + elif delta_cost > 0.0 or self.update_unstable: + # If cost is increased, increase lambda + self.lamb *= self.lamb_factor + + print(f'Cost increased by {-delta_cost}.') + print('Set feedforward term and controller gain to that from the previous iteration.') + print(f'Increased lambda to {self.lamb}.') + print(f'Current policy is from iteration {self.best_iteration}.') + + # Reset feedforward term and controller gain to that from + # the previous iteration. + self.input_ff = np.copy(self.input_ff_best) + self.gains_fb = np.copy(self.gains_fb_best) + + # Set improved flag to False. + self.prev_ite_improved = False + + # Break if maximum lambda is reached. + if self.lamb > self.lamb_max: + print(colored('Maximum lambda reached.', 'red')) + self.lamb = self.lamb_max + + # Reset update_unstable flag to False. + self.update_unstable = False + elif delta_cost <= 0.0: + # Save feedforward term and gain and state and input stacks. + self.best_iteration = self.ite_counter + self.previous_total_cost = self.total_cost + self.input_ff_best = np.copy(self.input_ff) + self.gains_fb_best = np.copy(self.gains_fb) + + # Check consecutive cost increment (cost decrement). + if abs(delta_cost) < self.epsilon and self.prev_ite_improved: + # Cost converged. + print(colored(f'iLQR cost converged with a tolerance of {self.epsilon}.', 'yellow')) + break + + # Set improved flag to True. + self.prev_ite_improved = True + + # Update controller gains + self.update_policy(env) + + self.ite_counter += 1 + + def update_policy(self, env): + '''Updates policy. Args: - state_stack (np.array): States from previous rollout. - input_stack (np.array): Inputs from previous rollout. - - """ - print(colored("UPDATE POLICY", "blue")) + env (BenchmarkEnv): The environment to be used for training. + ''' # Get symbolic loss function which also contains the necessary Jacobian # and Hessian of the loss w.r.t. state and input. loss = self.model.loss # Initialize backward pass. - state_k = state_stack[-1] - input_k = self.env.U_GOAL + state_k = self.state_stack[-1] + input_k = self.model.U_EQ - if self.task == Task.STABILIZATION: - x_goal = self.x_0 - elif self.task == Task.TRAJ_TRACKING: - x_goal = self.x_0[-1] + if env.TASK == Task.STABILIZATION: + x_goal = self.env.X_GOAL + elif env.TASK == Task.TRAJ_TRACKING: + x_goal = self.env.X_GOAL[-1] loss_k = loss(x=state_k, u=input_k, Xr=x_goal, - Ur=self.env.U_GOAL, + Ur=self.model.U_EQ, Q=self.Q, R=self.R) - s = loss_k["l"].toarray() - Sv = loss_k["l_x"].toarray().transpose() - Sm = loss_k["l_xx"].toarray().transpose() + s = loss_k['l'].toarray() + Sv = loss_k['l_x'].toarray().transpose() + Sm = loss_k['l_xx'].toarray().transpose() # Backward pass. for k in reversed(range(self.num_steps)): - print(k, self.num_steps, np.shape(state_stack), np.shape(input_stack), np.shape(self.gains_fb)) # Get current operating point. - state_k = state_stack[k] - input_k = input_stack[k] + state_k = self.state_stack[k] + input_k = self.input_stack[k] # Linearized dynamics about (x_k, u_k). df_k = self.model.df_func(state_k, input_k) @@ -415,24 +216,24 @@ def update_policy(self, state_stack, input_stack): # Get symbolic loss function that includes the necessary Jacobian # and Hessian of the loss w.r.t. state and input. - if self.task == Task.STABILIZATION: - x_goal = self.x_0 - elif self.task == Task.TRAJ_TRACKING: - x_goal = self.x_0[k] + if env.TASK == Task.STABILIZATION: + x_goal = self.env.X_GOAL + elif env.TASK == Task.TRAJ_TRACKING: + x_goal = self.env.X_GOAL[k] loss_k = loss(x=state_k, u=input_k, Xr=x_goal, - Ur=self.env.U_GOAL, + Ur=self.model.U_EQ, Q=self.Q, R=self.R) # Quadratic approximation of cost. - q = loss_k["l"].toarray() # l - Qv = loss_k["l_x"].toarray().transpose() # dl/dx - Qm = loss_k["l_xx"].toarray().transpose() # ddl/dxdx - Rv = loss_k["l_u"].toarray().transpose() # dl/du - Rm = loss_k["l_uu"].toarray().transpose() # ddl/dudu - Pm = loss_k["l_xu"].toarray().transpose() # ddl/dudx + q = loss_k['l'].toarray() # l + Qv = loss_k['l_x'].toarray().transpose() # dl/dx + Qm = loss_k['l_xx'].toarray().transpose() # ddl/dxdx + Rv = loss_k['l_u'].toarray().transpose() # dl/du + Rm = loss_k['l_uu'].toarray().transpose() # ddl/dudu + Pm = loss_k['l_xu'].toarray().transpose() # ddl/dudx # Control dependent terms of cost function. g = Rv + Bd_k.transpose().dot(Sv) @@ -458,179 +259,116 @@ def update_policy(self, state_stack, input_stack): # Update s variables for time step k. Sm = Qm + Ad_k.transpose().dot(Sm.dot(Ad_k)) + \ - K.transpose().dot(H.dot(K)) + \ - K.transpose().dot(G) + G.transpose().dot(K) + K.transpose().dot(H.dot(K)) + \ + K.transpose().dot(G) + G.transpose().dot(K) Sv = Qv + Ad_k.transpose().dot(Sv) + \ - K.transpose().dot(H.dot(duff)) + K.transpose().dot(g) + \ - G.transpose().dot(duff) + K.transpose().dot(H.dot(duff)) + K.transpose().dot(g) + \ + G.transpose().dot(duff) s = q + s + 0.5 * duff.transpose().dot(H.dot(duff)) + \ duff.transpose().dot(g) else: self.update_unstable = True - print(colored("Policy update unstable. Terminate update.", "red")) - def select_action(self, x, k): - """Control input u = -K x. + def select_action(self, obs, info=None, training=False): + '''Determine the action to take at the current timestep. Args: - x (np.array): Current state of the system. - k (int): Current time step. + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. Returns: - action (np.array): Action computed based on current policy. - - """ - if self.ite_counter == 0: - # Compute gain for the first iteration. - # action = -self.gain @ (x - self.x_0) + self.u_0 - if self.task == Task.STABILIZATION: - gains_fb = -self.gain - input_ff = self.gain @ self.x_0 + self.u_0 - - elif self.task == Task.TRAJ_TRACKING: - self.gain = compute_lqr_gain(self.model, self.x_0[k], - self.u_0, self.Q, self.R, - self.discrete_dynamics) - gains_fb = -self.gain - input_ff = self.gain @ self.x_0[k] + self.u_0 - else: - print(colored("Incorrect task specified.", "red")) - - # Compute action - action = gains_fb.dot(x) + input_ff - - # Save gains and feedforward term - if self.k == 0: - self.gains_fb = gains_fb.reshape(1, self.model.nu, self.model.nx) - self.input_ff = input_ff.reshape(self.model.nu, 1) + action (ndarray): The action chosen by the controller. + ''' + + step = self.extract_step(info) + + if training: + if self.ite_counter == 0: + action, gains_fb, input_ff = self.calculate_lqr_action(obs, step) + # Save gains and feedforward term + if step == 0: + self.gains_fb = gains_fb.reshape((1, self.model.nu, self.model.nx)) + self.input_ff = input_ff.reshape(self.model.nu, 1) + else: + self.gains_fb = np.append(self.gains_fb, gains_fb.reshape((1, self.model.nu, self.model.nx)), axis=0) + self.input_ff = np.append(self.input_ff, input_ff.reshape(self.model.nu, 1), axis=1) else: - self.gains_fb = np.append(self.gains_fb, gains_fb.reshape(1, self.model.nu, self.model.nx), axis=0) - self.input_ff = np.append(self.input_ff, input_ff.reshape(self.model.nu, 1), axis=1) + action = self.gains_fb[step].dot(obs) + self.input_ff[:, step] + elif self.gains_fb_best is not None: + action = self.gains_fb_best[step].dot(obs) + self.input_ff_best[:, step] else: - print(k, self.gains_fb[k]) - action = self.gains_fb[k].dot(x) + self.input_ff[:, k] + action, _, _ = self.calculate_lqr_action(obs, step) return action - def init_env(self): - self.env = self.env_func(randomized_init=self.random_init, - cost=Cost.QUADRATIC, - randomized_inertial_prop=False, - episode_len_sec=self.episode_len_sec, - task=self.task, - task_info=self.task_info, - ctrl_freq=self.ctrl_freq, - pyb_freq=self.pyb_freq - ) - self.env = RecordEpisodeStatistics(self.env, self.deque_size) + def calculate_lqr_action(self, obs, step): + '''Compute gain for the first iteration. + action = -self.gain @ (x - self.x_goal) + self.model.U_EQ - # Controller params. - self.model = self.env.symbolic - self.Q = get_cost_weight_matrix(self.q_lqr, self.model.nx) - self.R = get_cost_weight_matrix(self.r_lqr, self.model.nu) - self.env.set_cost_function_param(self.Q, self.R) + Args: + obs (ndarray): The observation at this timestep. + step (int): The timestep. + + Returns: + action (ndarray): The calculated action. + gains_fb (ndarray): The feedback gains. + input_ff (ndarray): The feedforward term. + ''' + if self.env.TASK == Task.STABILIZATION: + gains_fb = -self.gain + input_ff = self.gain @ self.env.X_GOAL + self.model.U_EQ + elif self.env.TASK == Task.TRAJ_TRACKING: + gains_fb = -self.gain + input_ff = self.gain @ self.env.X_GOAL[step] + self.model.U_EQ + + # Compute action + action = gains_fb.dot(obs) + input_ff + + return action, gains_fb, input_ff + + def reset(self): + '''Prepares for evaluation.''' self.env.reset() + self.ite_counter = 0 - # Linearize at operating point (equilibrium for stabilization). - self.x_0, self.u_0 = self.env.X_GOAL, self.env.U_GOAL + def run(self, env=None, max_steps=500, training=True): + '''Runs evaluation with current policy. - if self.task == Task.STABILIZATION: - self.gain = compute_lqr_gain(self.model, self.x_0, self.u_0, - self.Q, self.R, self.discrete_dynamics) + Args: + env (BenchmarkEnv): Environment for the task. + max_steps (int): Maximum number of steps. + training (bool): Whether the algorithm is training or evaluating. + ''' - # Control stepsize. - self.stepsize = self.model.dt + if env is None: + env = self.env - def reset_env(self): - '''Reset environment between iLQR iterations.''' - - print(colored("Set maximum episode length to %.3f" % self.episode_len_sec, "blue")) - self.env = self.env_func(init_state=self.init_state, - randomized_init=False, - cost=Cost.QUADRATIC, - randomized_inertial_prop=False, - episode_len_sec=self.episode_len_sec, - task=self.task, - task_info=self.task_info, - ctrl_freq=self.ctrl_freq, - pyb_freq=self.pyb_freq - ) - self.env = RecordEpisodeStatistics(self.env, self.deque_size) + # Reseed for batch-wise consistency. + obs, info = env.reset() + total_cost = 0.0 - # Controller params. - self.model = self.env.symbolic - self.Q = get_cost_weight_matrix(self.q_lqr, self.model.nx) - self.R = get_cost_weight_matrix(self.r_lqr, self.model.nu) - self.env.set_cost_function_param(self.Q, self.R) - self.env.reset() + for step in range(max_steps): + # Select action. + action = self.select_action(obs=obs, info=info, training=training) - # Linearize at operating point (equilibrium for stabilization). - self.x_0, self.u_0 = self.env.X_GOAL, self.env.U_GOAL + # Save rollout data. + if step == 0: + # Initialize state and input stack. + self.state_stack = obs + self.input_stack = action + else: + # Save state and input. + self.state_stack = np.vstack((self.state_stack, obs)) + self.input_stack = np.vstack((self.input_stack, action)) - def run(self, n_episodes=1, render=False, logging=False, verbose=False, use_adv=False): - """Runs evaluation with current policy. + # Step forward. + obs, cost, done, info = env.step(action) + total_cost -= cost - Args: - render (bool): Flag to save frames for visualization. - logging (bool): Flag to log results. + if done: + print(f'SUCCESS: Reached goal on step {step}. Terminating...') + break - Returns: - eval_results (dict): Dictionary containing returns and data for each - evaluation trial. - - """ - # Initialize logging variables. - ep_returns, ep_lengths, ep_fulldata, frames = [], [], {}, [] - - # Loop through episode. - for self.ep_counter in range(self.eval_batch_size): - # Initialize new environment for the test trial. - self.init_env() - - # Run iLQR for the particular initial condition. - ilqr_eval_results = self.run_ilqr(render=render, logging=logging) - - # Save the results from the last iteration for evaluation. - ep_returns.append(ilqr_eval_results["ite_returns"][-1]) - ep_lengths.append(ilqr_eval_results["ite_lengths"][-1]) - ep_fulldata["run%d_data" - % self.ep_counter] = ilqr_eval_results["ite_data"] - if "frames" in ilqr_eval_results: - frames.extend(np.asarray(ilqr_eval_results["frames"][-1])) - - # Print episode reward. - print(colored("Test Run %d reward %.4f" % (self.ep_counter, ep_returns[-1]), "yellow")) - print(colored("==========================\n", "yellow")) - - # Save reward - if self.save_data: - np.savetxt(self.data_dir + "test%d_rewards.csv" % self.ep_counter, np.array([ep_returns[-1]]), delimiter=',', fmt='%.8f') - - # Collect evaluation results. - ep_lengths = np.asarray(ep_lengths) - ep_returns = np.asarray(ep_returns) - - # Log data. - if logging: - msg = "****** Evaluation ******\n" - msg += "eval_ep_length {:.2f} +/- {:.2f} | " + \ - "eval_ep_return {:.3f} +/- {:.3f}\n".format( - ep_lengths.mean(), ep_lengths.std(), ep_returns.mean(), - ep_returns.std()) - self.logger.info(msg + "\n") - - # Save evaluation results. - # Note: To retrieve the state and input trajectories, use the following - # eval_results["ep_fulldata"]["run#_data"]["ite#_state"] - # eval_results["ep_fulldata"]["run#_data"]["ite#_input"] - eval_results = { - "ep_returns": ep_returns, - "ep_lengths": ep_lengths, - "ep_fulldata": ep_fulldata - } - - # Save frames. - if frames is not None and len(frames) > 0: - eval_results["frames"] = frames - - return eval_results + self.final_obs = obs + self.final_info = info + self.total_cost = total_cost diff --git a/safe_control_gym/controllers/lqr/ilqr.yaml b/safe_control_gym/controllers/lqr/ilqr.yaml index b2a1ef959..b9bbcb899 100644 --- a/safe_control_gym/controllers/lqr/ilqr.yaml +++ b/safe_control_gym/controllers/lqr/ilqr.yaml @@ -1,58 +1,14 @@ -# Model arguments -# Note: Higher simulation frequency is required if using controller designed -# based on the continuous-time model -discrete_dynamics: 1 - -# Runner arguments -deque_size: 10 -eval_batch_size: 1 - # Cost parameters q_lqr: [1] r_lqr: [0.1] +# Model arguments +# Note: Higher simulation frequency is required if using controller designed +# based on the continuous-time model +discrete_dynamics: True + # iLQR arguments max_iterations: 15 lamb_factor: 10 lamb_max: 1000 -epsilon: 0.001 - -# Shared/base arguments -verbose: False - -# Plot -plot_traj: False -save_plot: True -plot_dir: "./results/ilqr_baseline/" - -# Export csv files -save_data: True -data_dir: "./results/ilqr_baseline/" - - - - - - - - - - -# Task (options: stabilization, traj_tracking) -task: traj_tracking -episode_len_sec: 5 # episode maximum length in seconds -task_info: { - "stabilization_goal": [0, 1], - "trajectory_type": "circle", - "num_cycles": 1, - "trajectory_plane": "zx", - "trajectory_position_offset": [0.5, 0], - "trajectory_scale": -0.5 -} - -# Use random initial state -random_init: False - -# Control and simulation frequency -ctrl_freq: 100 -pyb_freq: 1000 \ No newline at end of file +epsilon: 0.01 diff --git a/safe_control_gym/controllers/lqr/lqr.py b/safe_control_gym/controllers/lqr/lqr.py index e33b9a4f2..5e9597d51 100644 --- a/safe_control_gym/controllers/lqr/lqr.py +++ b/safe_control_gym/controllers/lqr/lqr.py @@ -1,346 +1,65 @@ -"""Linear Quadratic Regulator (LQR) +'''Linear Quadratic Regulator (LQR).''' -Example: - run lqr on cartpole balance: - - python3 experiments/main.py --func test --tag lqr_pendulum --algo lqr --task cartpole - - run lqr on quadrotor stabilization: - - python3 experiments/main.py --func test --tag lqr_quad --algo lqr --task quadrotor --q_lqr 0.1 - -""" -import os -import numpy as np -from collections import defaultdict -import scipy.linalg -from termcolor import colored -import matplotlib.pyplot as plt -from matplotlib.ticker import FormatStrFormatter - -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.utils.logging import ExperimentLogger from safe_control_gym.controllers.base_controller import BaseController -from safe_control_gym.controllers.lqr.lqr_utils import * -from safe_control_gym.envs.benchmark_env import Cost, Task +from safe_control_gym.controllers.lqr.lqr_utils import compute_lqr_gain, get_cost_weight_matrix +from safe_control_gym.envs.benchmark_env import Task class LQR(BaseController): - """Linear quadratic regulator. - - Attributes: - env (gym.Env): environment for the task. - Q, R (np.array): cost weight matrix. - x_0, u_0 (np.array): equilibrium state & input. - gain (np.array): input gain matrix. - - """ + '''Linear quadratic regulator.''' def __init__( self, env_func, # Model args. - q_lqr=[1], - r_lqr=[1], - discrete_dynamics=1, - # Runner args. - deque_size=10, - eval_batch_size=1, - # Task - task: Task = Task.STABILIZATION, - task_info=None, - episode_len_sec=10, - # Shared/base args. - output_dir="results/temp", - verbose=False, - model_step_chk=False, - random_init=True, - ctrl_freq=240, - pyb_freq=240, - save_data=False, - data_dir=None, - plot_traj=False, - plot_dir=None, - save_plot=False, - init_state_randomization_info=None, + q_lqr: list = None, + r_lqr: list = None, + discrete_dynamics: bool = True, **kwargs): - """Creates task and controller. + '''Creates task and controller. Args: - env_func (Callable): function to instantiate task/environment. - q_lqr (list): diagonals of state cost weight. - r_lqr (list): diagonals of input/action cost weight. - discrete_dynamics (int): if to use discrete or continuous dynamics. - deque_size (int): number of episodes to average over per evaluation statistic. - eval_batch_size (int): number of episodes to run for evaluation. - output_dir (str): output directory to write logs and results. - - """ - # All params/args. - for k, v in locals().items(): - if k != "self" and k != "kwargs" and "__" not in k: - self.__dict__[k] = v + env_func (Callable): Function to instantiate task/environment. + q_lqr (list): Diagonals of state cost weight. + r_lqr (list): Diagonals of input/action cost weight. + discrete_dynamics (bool): If to use discrete or continuous dynamics. + ''' - # Task. - self.task = Task(task) - self.env_func = env_func - self.cost = Cost.QUADRATIC - self.init_state_randomization_info = init_state_randomization_info - self.episode_len_sec = episode_len_sec - self.task = task - self.task_info = task_info - self.ctrl_freq = ctrl_freq - self.pyb_freq = pyb_freq - self.deque_size = deque_size - self.random_init = random_init - self.env = env_func(cost=self.cost, - randomized_init=random_init, - init_state_randomization_info=init_state_randomization_info, - randomized_inertial_prop=False, - episode_len_sec=episode_len_sec, - task=task, - task_info=task_info, - ctrl_freq=ctrl_freq, - pyb_freq=pyb_freq) - self.env = RecordEpisodeStatistics(self.env, deque_size) + super().__init__(env_func, **kwargs) + self.env = env_func() # Controller params. - self.model = self.env.symbolic - self.Q = get_cost_weight_matrix(self.q_lqr, self.model.nx) - self.R = get_cost_weight_matrix(self.r_lqr, self.model.nu) - self.env.set_cost_function_param(self.Q, self.R) - - # Linearize at operating point (equilibrium for stabilization). - self.x_0, self.u_0 = self.env.X_GOAL, self.env.U_GOAL + self.model = self.get_prior(self.env) self.discrete_dynamics = discrete_dynamics + self.Q = get_cost_weight_matrix(q_lqr, self.model.nx) + self.R = get_cost_weight_matrix(r_lqr, self.model.nu) + self.env.set_cost_function_param(self.Q, self.R) - if self.task == Task.STABILIZATION: - self.gain = compute_lqr_gain(self.model, self.x_0, self.u_0, - self.Q, self.R, self.discrete_dynamics) - - # Model step for debugging - # self.env.reset() - self.stepsize = self.model.dt - - # Check model step flag. - self.model_step_chk = model_step_chk - - # Plot trajectory. - self.plot_traj = plot_traj - - # Save plot. - self.save_plot = save_plot - - # Plot output directory. - self.plot_dir = plot_dir - if not os.path.exists(plot_dir): - os.makedirs(plot_dir) - - # Save data. - self.save_data = save_data - - # Data output directory. - self.data_dir = data_dir - if self.data_dir: - if not os.path.exists(data_dir): - os.makedirs(data_dir) - else: - self.save_data = False - - # Logging. - self.logger = ExperimentLogger(output_dir) + self.gain = compute_lqr_gain(self.model, self.model.X_EQ, self.model.U_EQ, + self.Q, self.R, self.discrete_dynamics) - def model_step(self): - model = self.env.symbolic - self.model_input = self.select_action(self.model_state) - self.state_dot = model.fc_func(self.model_state, self.model_input) - self.model_state = self.model_state + self.stepsize * self.state_dot + def reset(self): + '''Prepares for evaluation.''' + self.env.reset() def close(self): - """Cleans up resources.""" + '''Cleans up resources.''' self.env.close() - self.logger.close() - def select_action(self, x): - """Calculates control input u = -K x. + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. Args: - x (np.array): step-wise observation/input. + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. Returns: - np.array: step-wise control input/actino. - - """ - if self.task == Task.STABILIZATION: - return -self.gain @ (x - self.x_0) + self.u_0 - elif self.task == Task.TRAJ_TRACKING: - self.gain = compute_lqr_gain(self.model, self.x_0[self.k], - self.u_0, self.Q, self.R, - self.discrete_dynamics) - return -self.gain @ (x - self.x_0[self.k]) + self.u_0 - else: - print(colored("Incorrect task specified.", "red")) - - def compute_lqr_gain(self, x_0, u_0): - # Linearization. - df = self.model.df_func(x_0, u_0) - A, B = df[0].toarray(), df[1].toarray() - - # Compute controller gain. - if self.discrete_dynamics: - # x[k+1] = A x[k] + B u[k] - A, B = discretize_linear_system(A, B, self.model.dt) - P = scipy.linalg.solve_discrete_are(A, B, self.Q, self.R) - btp = np.dot(B.T, P) - gain = np.dot(np.linalg.inv(self.R + np.dot(btp, B)), - np.dot(btp, A)) - else: - # dx/dt = A x + B u - P = scipy.linalg.solve_continuous_are(A, B, self.Q, self.R) - gain = np.dot(np.linalg.inv(self.R), np.dot(B.T, P)) - - return gain - - def run(self, n_episodes=1, render=False, logging=False, verbose=False, use_adv=False): - """Runs evaluation with current policy. - - Args: - render (bool): if to render during the runs. - logging (bool): if to log using logger during the runs. - - Returns: - dict: evaluation results - - """ - ep_returns, ep_lengths = [], [] - frames = [] - self.ep_counter = 0 - self.k = 0 - - # Reseed for batch-wise consistency. - obs = self.env.reset() - ep_seed = 1 #self.env.SEED - - while len(ep_returns) < self.eval_batch_size: - # Current goal. - if self.task == Task.STABILIZATION: - current_goal = self.x_0 - elif self.task == Task.TRAJ_TRACKING: - current_goal = self.x_0[self.k] - - # Select action. - action = self.select_action(self.env.state) - - # Save initial condition. - if self.k == 0: - x_init = self.env.state - if self.model_step_chk: - self.model_state = self.env.state - - # Initialize state and input stack. - state_stack = self.env.state - input_stack = action - goal_stack = current_goal - - # Print initial state. - print(colored("initial state (%d): " % ep_seed + get_arr_str(self.env.state), "green")) - - else: - # Save state and input. - state_stack = np.vstack((state_stack, self.env.state)) - input_stack = np.vstack((input_stack, action)) - goal_stack = np.vstack((goal_stack, current_goal)) - - # Step forward. - obs, reward, done, info = self.env.step(action) - - # Debug with analytical model. - if self.model_step_chk: - self.model_step() - - # Update step counter - self.k += 1 - - if verbose: - if self.task == Task.TRAJ_TRACKING: - print("goal state: " + get_arr_str(self.x_0)) - print("state: " + get_arr_str(self.env.state)) - if self.model_step_chk: - print("model_state: " + get_arr_str(self.model_state)) - print("obs: " + get_arr_str(obs)) - print("action: " + get_arr_str(action) + "\n") - - if render: - self.env.render() - frames.append(self.env.render("rgb_array")) - - if done: - # Push last state and input to stack. - # Note: the last input is not used. - state_stack = np.vstack((state_stack, self.env.state)) - input_stack = np.vstack((input_stack, action)) - goal_stack = np.vstack((goal_stack, current_goal)) - - # Post analysis. - if self.plot_traj or self.save_plot or self.save_data: - analysis_data = post_analysis(goal_stack, state_stack, - input_stack, self.env, 0, - self.ep_counter, - self.plot_traj, - self.save_plot, - self.save_data, - self.plot_dir, self.data_dir) - if self.ep_counter == 0: - ep_rmse = np.array([analysis_data["state_rmse_scalar"]]) - else: - ep_rmse = np.vstack((ep_rmse, analysis_data["state_rmse_scalar"])) - - # Update iteration return and length lists. - assert "episode" in info - ep_returns.append(info["episode"]["r"]) - ep_lengths.append(info["episode"]["l"]) - - print(colored("Test Run %d reward %.2f" % (self.ep_counter, ep_returns[-1]), "yellow")) - print(colored("initial state: " + get_arr_str(x_init), "yellow")) - if self.task == Task.STABILIZATION: - print(colored("final state: " + get_arr_str(self.env.state), "yellow")) - print(colored("goal state: " + get_arr_str(self.x_0), "yellow")) - print(colored("==========================\n", "yellow")) - - # Save reward - if self.save_data: - np.savetxt(self.data_dir + "test%d_rewards.csv" % self.ep_counter, np.array([ep_returns[-1]]), delimiter=',', fmt='%.8f') - - self.ep_counter += 1 - ep_seed += 1 - self.k = 0 - self.env = self.env_func(cost=self.cost, - randomized_init=self.random_init, - seed=ep_seed, - init_state_randomization_info=self.init_state_randomization_info, - randomized_inertial_prop=False, - episode_len_sec=self.episode_len_sec, - task=self.task, - task_info=self.task_info, - ctrl_freq=self.ctrl_freq, - pyb_freq=self.pyb_freq) - self.env = RecordEpisodeStatistics(self.env, self.deque_size) - obs = self.env.reset() - - # Collect evaluation results. - ep_lengths = np.asarray(ep_lengths) - ep_returns = np.asarray(ep_returns) - if logging: - msg = "****** Evaluation ******\n" - msg += "eval_ep_length {:.2f} +/- {:.2f} | eval_ep_return {:.3f} +/- {:.3f}\n".format( - ep_lengths.mean(), ep_lengths.std(), ep_returns.mean(), - ep_returns.std()) - self.logger.info(msg + "\n") + action (ndarray): The action chosen by the controller. + ''' - if self.save_data: - np.savetxt(self.data_dir + "all_test_mean_rmse.csv", ep_rmse, delimiter=',', fmt='%.8f') + step = self.extract_step(info) - eval_results = {"ep_returns": ep_returns, "ep_lengths": ep_lengths} - if len(frames) > 0 and frames[0] is not None: - eval_results["frames"] = frames - return eval_results + if self.env.TASK == Task.STABILIZATION: + return -self.gain @ (obs - self.env.X_GOAL) + self.model.U_EQ + elif self.env.TASK == Task.TRAJ_TRACKING: + return -self.gain @ (obs - self.env.X_GOAL[step]) + self.model.U_EQ diff --git a/safe_control_gym/controllers/lqr/lqr.yaml b/safe_control_gym/controllers/lqr/lqr.yaml index b776a33c9..3388df2fc 100644 --- a/safe_control_gym/controllers/lqr/lqr.yaml +++ b/safe_control_gym/controllers/lqr/lqr.yaml @@ -2,66 +2,7 @@ q_lqr: [1] r_lqr: [0.1] -# Verbose -verbose: True - # Model arguments # Note: Higher simulation frequency is required if using controller designed -# based on the continuous-time model -discrete_dynamics: 1 - -# Runner arguments -deque_size: 10 - -# Number of test runs -eval_batch_size: 10 - -# Model step (step with analytical model for debugging) -model_step_chk: False - -# Plot -plot_traj: False -save_plot: True -plot_dir: "./results/lqr_baseline/" - -# Export csv files -save_data: True -data_dir: "./results/lqr_baseline/" - - - - - - - - -# Task (options: stabilization, traj_tracking) -task: stabilization -episode_len_sec: 5 # episode maximum length in seconds -task_info: { - "stabilization_goal": [0, 1], - "trajectory_type": "circle", - "num_cycles": 1, - "trajectory_plane": "zx", - "trajectory_position_offset": [0.5, 0], - "trajectory_scale": -0.5, - "stabilization_goal_tolerance": 0.01 -} - -# Use random initial state -random_init: True - -# Control and simulation frequency -ctrl_freq: 50 -pyb_freq: 1000 - -# Initial state sampling -init_state_randomization_info: - init_theta: - distrib: uniform - high: 0.1 - low: -0.1 - init_x: - distrib: uniform - high: 0.3 - low: -0.3 +# based on the continuous-time model +discrete_dynamics: True diff --git a/safe_control_gym/controllers/lqr/lqr_utils.py b/safe_control_gym/controllers/lqr/lqr_utils.py index f0bdd82d5..9bcd5919e 100644 --- a/safe_control_gym/controllers/lqr/lqr_utils.py +++ b/safe_control_gym/controllers/lqr/lqr_utils.py @@ -1,21 +1,24 @@ -"""Linear Quadratic Regulator (LQR) utilities +'''Linear Quadratic Regulator (LQR) utilities.''' -""" import numpy as np -import scipy -from termcolor import colored -import matplotlib.pyplot as plt -from matplotlib.ticker import FormatStrFormatter +import scipy.linalg -def get_arr_str(vector, str_format_arg=".2f"): - str_format = "{:%s}" % str_format_arg - vector_str = [str_format.format(i) for i in vector] - vector_str_joined = " ".join(vector_str) - return vector_str_joined +def compute_lqr_gain(model, x_0, u_0, Q, R, discrete_dynamics=True): + '''Computes the LQR gain from the model, linearization points, and Q and R matrices. + Args: + model (SymbolicModel): The SymbolicModel of the system. + x_0 (ndarray): The linearization point of the state X. + u_0 (ndarray): The linearization point of the input U. + Q (ndarray): The state cost matrix Q. + R (ndarray): The input cost matrix R. + discrete_dynamics (bool): If to use discrete or continuous dynamics. + + Returns: + gain (ndarray): The LQR gain for the system. + ''' -def compute_lqr_gain(model, x_0, u_0, Q, R, discrete_dynamics=True): # Linearization. df = model.df_func(x_0, u_0) A, B = df[0].toarray(), df[1].toarray() @@ -37,21 +40,22 @@ def compute_lqr_gain(model, x_0, u_0, Q, R, discrete_dynamics=True): def discretize_linear_system(A, B, dt, exact=False): - """ discretization of a linear system - - dx/dt = A x + B u + '''Discretization of a linear system + + dx/dt = A x + B u --> xd[k+1] = Ad xd[k] + Bd ud[k] where xd[k] = x(k*dt) Args: - A: np.array, system transition matrix - B: np.array, input matrix - dt: scalar, step time interval - exact: bool, if to use exact discretization + A (ndarray): System transition matrix. + B (ndarray): Input matrix. + dt (scalar): Step time interval. + exact (bool): If to use exact discretization. Returns: - discretized matrices Ad, Bd + Ad (ndarray): The discrete linear state matrix A. + Bd (ndarray): The discrete linear input matrix B. + ''' - """ state_dim, input_dim = A.shape[1], B.shape[1] if exact: @@ -63,120 +67,28 @@ def discretize_linear_system(A, B, dt, exact=False): Ad = Md[:state_dim, :state_dim] Bd = Md[:state_dim, state_dim:] else: - I = np.eye(state_dim) - Ad = I + A * dt + Identity = np.eye(state_dim) + Ad = Identity + A * dt Bd = B * dt return Ad, Bd def get_cost_weight_matrix(weights, dim): - """Gets weight matrix from input args. + '''Gets weight matrix from input args. + + Args: + weights (list): A 1D list of weights. + dim (int): The dimension of the desired cost weight matrix. + + Returns: + W (ndarray): The cost weight matrix. + ''' - """ if len(weights) == dim: W = np.diag(weights) elif len(weights) == 1: W = np.diag(weights * dim) else: - raise Exception("Wrong dimension for cost weights.") + raise Exception('Wrong dimension for cost weights.') return W - -def post_analysis(goal_stack, state_stack, input_stack, env, - ite_counter, ep_counter, plot_traj, save_plot, save_data, - plot_dir, data_dir): - # Get model - model = env.symbolic - stepsize = model.dt - - # Get times - plot_length = np.min([np.shape(goal_stack)[0], np.shape(state_stack)[0]]) - times = np.linspace(0, stepsize * plot_length, plot_length) - - # Plot states - fig, axs = plt.subplots(model.nx) - if model.nx == 1: - axs = [axs] - for k in range(model.nx): - axs[k].plot(times, state_stack.transpose()[k, 0:plot_length], label='actual') - axs[k].plot(times, goal_stack.transpose()[k, 0:plot_length], color='r', label='desired') - axs[k].set(ylabel=env.STATE_LABELS[k] + '\n[%s]' % env.STATE_UNITS[k]) - axs[k].yaxis.set_major_formatter(FormatStrFormatter('%.1f')) - if not (k == model.nx - 1): - axs[k].set_xticks([]) - axs[0].set_title('State Trajectories') - axs[-1].legend(ncol=3, bbox_transform=fig.transFigure, bbox_to_anchor=(1, 0), loc="lower right") - axs[-1].set(xlabel='time (sec)') - if save_plot: - plt.savefig(plot_dir + "state_ite%d" % ite_counter) - - # Plot inputs - _, axs = plt.subplots(model.nu) - if model.nu == 1: - axs = [axs] - for k in range(model.nu): - axs[k].plot(times, input_stack.transpose()[k, 0:plot_length]) - axs[k].set(ylabel='input %d' % k) - axs[k].set(ylabel=env.ACTION_LABELS[k] + '\n[%s]' % env.ACTION_UNITS[k]) - axs[k].yaxis.set_major_formatter(FormatStrFormatter('%.1f')) - axs[0].set_title('Input Trajectories') - axs[-1].set(xlabel='time (sec)') - - # Compute RMSE for each state - state_error = state_stack.transpose()[:, 0:plot_length].transpose() -\ - goal_stack.transpose()[:, 0:plot_length].transpose() - - # Check if state is an angle and wrap angle error to [-pi, pi] - angle_state_index = [i for i, x in enumerate(env.STATE_UNITS) if x == "rad"] - for k in angle_state_index: - state_error[:, k] = wrap2pi_vec(state_error[:, k]) - - state_rmse, state_rmse_scalar = compute_state_rmse(state_error) - - if plot_traj: - plt.show() - - if save_plot: - plt.savefig(plot_dir + "input_ite%d" % ite_counter) - plt.close() - - if save_data: - np.savetxt(data_dir + "test%d_times.csv" % ep_counter, times.transpose(), delimiter=',', fmt='%.8f') - np.savetxt(data_dir + "test%d_states.csv" % ep_counter, state_stack.transpose()[:, 0:plot_length].transpose(), delimiter=',', fmt='%.8f') - np.savetxt(data_dir + "test%d_states_des.csv" % ep_counter, goal_stack.transpose()[:, 0:plot_length].transpose(), delimiter=',', fmt='%.8f') - np.savetxt(data_dir + "test%d_inputs.csv" % ep_counter, input_stack.transpose()[:, 0:plot_length].transpose(), delimiter=',', fmt='%.8f') - np.savetxt(data_dir + "test%d_state_rmse.csv" % ep_counter, state_rmse, delimiter=',', fmt='%.8f') - np.savetxt(data_dir + "test%d_state_rmse_scalar.csv" % ep_counter, np.array([state_rmse_scalar]), delimiter=',', fmt='%.8f') - - # Return analysis data - analysis_data = {} - analysis_data["state_rmse"] = state_rmse - analysis_data["state_rmse_scalar"] = state_rmse_scalar - - return analysis_data - - -def wrap2pi_vec(angle_vec): - for k in range(len(angle_vec)): - angle_vec[k] = wrap2pi(angle_vec[k]) - return angle_vec - - -def wrap2pi(angle): - while angle > np.pi: - angle -= np.pi - while angle <= -np.pi: - angle += np.pi - return angle - - -def compute_state_rmse(state_error): - # Compute root-mean-square error - mse = np.mean(state_error ** 2, axis=0) - state_rmse = np.sqrt(mse) - state_rmse_scalar = np.sqrt(np.sum(mse)) - # Print root-mean-square error - print(colored("rmse by state: " + get_arr_str(state_rmse), "blue")) - print(colored("scalarized rmse: %.2f" % state_rmse_scalar, "blue")) - return state_rmse, state_rmse_scalar - diff --git a/safe_control_gym/controllers/mpc/gp_mpc.py b/safe_control_gym/controllers/mpc/gp_mpc.py index 18b12065a..da626cf51 100644 --- a/safe_control_gym/controllers/mpc/gp_mpc.py +++ b/safe_control_gym/controllers/mpc/gp_mpc.py @@ -1,7 +1,7 @@ -"""Model Predictive Control with a Gaussian Process model. +'''Model Predictive Control with a Gaussian Process model. Based on: - * L. Hewing, J. Kabzan and M. N. Zeilinger, "Cautious Model Predictive Control Using Gaussian Process Regression," + * L. Hewing, J. Kabzan and M. N. Zeilinger, 'Cautious Model Predictive Control Using Gaussian Process Regression,' in IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2736-2743, Nov. 2020, doi: 10.1109/TCST.2019.2949757. Implementation details: @@ -15,31 +15,30 @@ Systems, Y. Weiss, B. Scholkopf, and J. C. Platt, Eds., 2006, pp. 1257–1264. and the inducing points are the previous MPC solution. 3. Each dimension of the learned error dynamics is an independent Zero Mean SE Kernel GP. +''' -""" -import munch -import scipy -import numpy as np -import casadi as cs -from copy import deepcopy import time -import torch -import gpytorch -from skopt.sampler import Lhs +from copy import deepcopy from functools import partial -from sklearn.model_selection import train_test_split + +import casadi as cs +import gpytorch +import numpy as np +import scipy +import torch from sklearn.metrics import pairwise_distances_argmin_min +from sklearn.model_selection import train_test_split +from skopt.sampler import Lhs -from safe_control_gym.controllers.mpc.linear_mpc import LinearMPC, MPC -from safe_control_gym.controllers.mpc.mpc_utils import discretize_linear_system -from safe_control_gym.controllers.mpc.gp_utils import GaussianProcessCollection, ZeroMeanIndependentGPModel, covSEard, kmeans_centriods +from safe_control_gym.controllers.lqr.lqr_utils import discretize_linear_system +from safe_control_gym.controllers.mpc.gp_utils import (GaussianProcessCollection, ZeroMeanIndependentGPModel, + covSEard, kmeans_centriods) +from safe_control_gym.controllers.mpc.linear_mpc import MPC, LinearMPC from safe_control_gym.envs.benchmark_env import Task -from safe_control_gym.envs.constraints import GENERAL_CONSTRAINTS -class GPMPC(MPC): - """MPC with Gaussian Process as dynamics residual. - """ +class GPMPC(MPC): + '''MPC with Gaussian Process as dynamics residual.''' def __init__( self, @@ -48,7 +47,7 @@ def __init__( horizon: int = 5, q_mpc: list = [1], r_mpc: list = [1], - constraint_tol: float=1e-8, + constraint_tol: float = 1e-8, additional_constraints: list = None, soft_constraints: dict = None, warmstart: bool = True, @@ -70,13 +69,14 @@ def __init__( inducing_point_selection_method: str = 'kmeans', recalc_inducing_points_at_every_step: bool = False, online_learning: bool = False, - inertial_prop: list = [1.0], + prior_info: dict = None, + # inertial_prop: list = [1.0], prior_param_coeff: float = 1.0, terminate_run_on_done: bool = True, - output_dir: str = "results/temp", + output_dir: str = 'results/temp', **kwargs - ): - """Initialize GP-MPC. + ): + '''Initialize GP-MPC. Args: env_func (gym.Env): functionalized initialization of the environment. @@ -95,7 +95,7 @@ def __init__( output_dir (str): directory to store model and results. prob (float): desired probabilistic safety level. initial_rollout_std (float): the initial std (across all states) for the mean_eq rollout. - inertial_prop (list): to initialize the inertial properties of the prior model. + prior_info (dict): Dictionary specifiy the algorithms prior model parameters. prior_param_coeff (float): constant multiplying factor to adjust the prior model intertial properties. input_mask (list): list of which input dimensions to use in GP model. If None, all are used. target_mask (list): list of which output dimensions to use in the GP model. If None, all are used. @@ -106,18 +106,12 @@ def __init__( recalc_inducing_points_at_every_step (bool): True to recompute the gp approx at every time step. online_learning (bool): if true, GP kernel values will be updated using past trajectory values. additional_constraints (list): list of Constraint objects defining additional constraints to be used. + ''' - """ - if type(inertial_prop) is list: - self.prior_env_func = partial(env_func, - inertial_prop=np.array(inertial_prop)*prior_param_coeff, - prior_prop=np.array(inertial_prop)*prior_param_coeff) - else: - assert (type(inertial_prop) is dict) or (type(inertial_prop) == munch.Munch), '[Error]: GPMPC.__init__(): Intertial properties must be a list, dict, or munch.Munch' - inertial_prop.update((prop, val*prior_param_coeff) for prop, val in inertial_prop.items()) - self.prior_env_func = partial(env_func, - inertial_prop=inertial_prop, - prior_prop=inertial_prop) + if prior_info is None or prior_info == {}: + raise ValueError('GPMPC requires prior_prop to be defined. You may use the real mass properties and then use prior_param_coeff to modify them accordingly.') + prior_info['prior_prop'].update((prop, val * prior_param_coeff) for prop, val in prior_info['prior_prop'].items()) + self.prior_env_func = partial(env_func, inertial_prop=prior_info['prior_prop']) if soft_constraints is None: self.soft_constraints_params = {'gp_soft_constraints': False, 'gp_soft_constraints_coeff': 0, @@ -135,12 +129,14 @@ def __init__( warmstart=warmstart, soft_constraints=self.soft_constraints_params['prior_soft_constraints'], terminate_run_on_done=terminate_run_on_done, + prior_info=prior_info, # runner args # shared/base args output_dir=output_dir, additional_constraints=additional_constraints, ) self.prior_ctrl.reset() + self.sparse_gp = sparse_gp super().__init__( self.prior_env_func, horizon=horizon, @@ -150,10 +146,13 @@ def __init__( soft_constraints=self.soft_constraints_params['gp_soft_constraints'], terminate_run_on_done=terminate_run_on_done, constraint_tol=constraint_tol, + prior_info=prior_info, # runner args # shared/base args output_dir=output_dir, additional_constraints=additional_constraints, + use_gpu=use_gpu, + seed=seed, **kwargs) # Setup environments. self.env_func = env_func @@ -164,8 +163,8 @@ def __init__( self.data_inputs = None self.data_targets = None self.prior_dynamics_func = self.prior_ctrl.linear_dynamics_func - self.X_EQ = np.atleast_2d(self.env.X_EQ)[0,:].T - self.U_EQ = np.atleast_2d(self.env.U_EQ)[0,:].T + self.X_EQ = self.prior_ctrl.X_EQ + self.U_EQ = self.prior_ctrl.U_EQ # GP and training parameters. self.gaussian_process = None self.train_iterations = train_iterations @@ -175,10 +174,7 @@ def __init__( self.learning_rate = learning_rate self.gp_model_path = gp_model_path self.normalize_training_data = normalize_training_data - self.use_gpu = use_gpu and torch.cuda.is_available() - self.seed = seed self.prob = prob - self.sparse_gp = sparse_gp if input_mask is None: self.input_mask = np.arange(self.model.nx + self.model.nu).tolist() else: @@ -203,9 +199,7 @@ def __init__( self.gp_soft_constraints_coeff = self.soft_constraints_params['gp_soft_constraints_coeff'] def setup_prior_dynamics(self): - """Computes the LQR gain used for propograting GP uncertainty from the prior model dynamics. - - """ + '''Computes the LQR gain used for propograting GP uncertainty from the prior model dynamics.''' # Determine the LQR gain K to propogate the input uncertainty (doing this at each timestep will increase complexity). A, B = discretize_linear_system(self.prior_ctrl.dfdx, self.prior_ctrl.dfdu, self.dt) Q_lqr = self.Q @@ -217,20 +211,17 @@ def setup_prior_dynamics(self): self.discrete_dfdu = B def set_gp_dynamics_func(self, n_ind_points): - """Updates symbolic dynamics. + '''Updates symbolic dynamics. With actual control frequency, initialize GP model and add to the combined dynamics. - - """ + ''' self.setup_prior_dynamics() # Compute the probabilistic constraint inverse CDF according to section III.D.b in Hewing 2019. - self.inverse_cdf = scipy.stats.norm.ppf(1 - (1/self.model.nx - (self.prob + 1)/(2*self.model.nx))) + self.inverse_cdf = scipy.stats.norm.ppf(1 - (1 / self.model.nx - (self.prob + 1) / (2 * self.model.nx))) self.create_sparse_GP_machinery(n_ind_points) def create_sparse_GP_machinery(self, n_ind_points): - """This setups the gaussian process approximations for FITC formulation. - - """ + '''This setups the gaussian process approximations for FITC formulation.''' lengthscales, signal_var, noise_var, gp_K_plus_noise = self.gaussian_process.get_hyperparameters(as_numpy=True) self.length_scales = lengthscales.squeeze() self.signal_var = signal_var.squeeze() @@ -238,14 +229,13 @@ def create_sparse_GP_machinery(self, n_ind_points): self.gp_K_plus_noise = gp_K_plus_noise Nx = len(self.input_mask) Ny = len(self.target_mask) - N = self.gaussian_process.n_training_samples # Create CasADI function for computing the kernel K_z_zind with parameters for z, z_ind, length scales and signal variance. # We need the CasADI version of this so that it can by symbolically differentiated in in the MPC optimization. z1 = cs.SX.sym('z1', Nx) z2 = cs.SX.sym('z2', Nx) ell_s = cs.SX.sym('ell', Nx) sf2_s = cs.SX.sym('sf2') - z_ind = cs.SX.sym('z_ind', n_ind_points, Nx) + z_ind = cs.SX.sym('z_ind', n_ind_points, Nx) covSE = cs.Function('covSE', [z1, z2, ell_s, sf2_s], [covSEard(z1, z2, ell_s, sf2_s)]) ks = cs.SX.zeros(1, n_ind_points) @@ -254,101 +244,99 @@ def create_sparse_GP_machinery(self, n_ind_points): ks_func = cs.Function('K_s', [z1, z_ind, ell_s, sf2_s], [ks]) K_z_zind = cs.SX.zeros(Ny, n_ind_points) for i in range(Ny): - K_z_zind[i,:] = ks_func(z1, z_ind, self.length_scales[i,:], self.signal_var[i]) + K_z_zind[i, :] = ks_func(z1, z_ind, self.length_scales[i, :], self.signal_var[i]) # This will be mulitplied by the mean_post_factor computed at every time step to compute the approximate mean. - self.K_z_zind_func = cs.Function('K_z_zind', [z1, z_ind],[K_z_zind],['z1', 'z2'],['K']) + self.K_z_zind_func = cs.Function('K_z_zind', [z1, z_ind], [K_z_zind], ['z1', 'z2'], ['K']) def preprocess_training_data(self, x_seq, u_seq, x_next_seq ): - """Converts trajectory data for GP trianing. - + '''Converts trajectory data for GP trianing. + Args: - x_seq (list): state sequence of np.array (nx,). - u_seq (list): action sequence of np.array (nu,). - x_next_seq (list): next state sequence of np.array (nx,). - + x_seq (list): state sequence of np.array (nx,). + u_seq (list): action sequence of np.array (nu,). + x_next_seq (list): next state sequence of np.array (nx,). + Returns: np.array: inputs for GP training, (N, nx+nu). np.array: targets for GP training, (N, nx). - - """ + ''' # Get the predicted dynamics. This is a linear prior, thus we need to account for the fact that # it is linearized about an eq using self.X_GOAL and self.U_GOAL. x_pred_seq = self.prior_dynamics_func(x0=x_seq.T - self.prior_ctrl.X_EQ[:, None], - p=u_seq.T - self.prior_ctrl.U_EQ[:,None])['xf'].toarray() - targets = (x_next_seq.T - (x_pred_seq+self.prior_ctrl.X_EQ[:,None])).transpose() # (N, nx). + p=u_seq.T - self.prior_ctrl.U_EQ[:, None])['xf'].toarray() + targets = (x_next_seq.T - (x_pred_seq + self.prior_ctrl.X_EQ[:, None])).transpose() # (N, nx). inputs = np.hstack([x_seq, u_seq]) # (N, nx+nu). return inputs, targets def precompute_probabilistic_limits(self, print_sets=False ): - """This updates the constraint value limits to account for the uncertainty in the dynamics rollout. + '''This updates the constraint value limits to account for the uncertainty in the dynamics rollout. Args: print_sets (bool): True to print out the sets for debugging purposes. - - """ + ''' nx, nu = self.model.nx, self.model.nu T = self.T - state_covariances = np.zeros((self.T+1, nx, nx)) + state_covariances = np.zeros((self.T + 1, nx, nx)) input_covariances = np.zeros((self.T, nu, nu)) # Initilize lists for the tightening of each constraint. state_constraint_set = [] for state_constraint in self.constraints.state_constraints: - state_constraint_set.append(np.zeros((state_constraint.num_constraints, T+1))) + state_constraint_set.append(np.zeros((state_constraint.num_constraints, T + 1))) input_constraint_set = [] for input_constraint in self.constraints.input_constraints: input_constraint_set.append(np.zeros((input_constraint.num_constraints, T))) if self.x_prev is not None and self.u_prev is not None: - #cov_x = np.zeros((nx, nx)) - cov_x = np.diag([self.initial_rollout_std**2]*nx) + # cov_x = np.zeros((nx, nx)) + cov_x = np.diag([self.initial_rollout_std**2] * nx) for i in range(T): state_covariances[i] = cov_x cov_u = self.lqr_gain @ cov_x @ self.lqr_gain.T input_covariances[i] = cov_u cov_xu = cov_x @ self.lqr_gain.T - z = np.hstack((self.x_prev[:,i], self.u_prev[:,i])) + z = np.hstack((self.x_prev[:, i], self.u_prev[:, i])) if self.gp_approx == 'taylor': - raise NotImplementedError("Taylor GP approximation is currently not working.") + raise NotImplementedError('Taylor GP approximation is currently not working.') elif self.gp_approx == 'mean_eq': - _, cov_d_tensor = self.gaussian_process.predict(z[None,:], return_pred=False) + _, cov_d_tensor = self.gaussian_process.predict(z[None, :], return_pred=False) cov_d = cov_d_tensor.detach().numpy() - # ToDo: Addition of noise here! And do we still need initial_rollout_std + # TODO: Addition of noise here! And do we still need initial_rollout_std _, _, cov_noise, _ = self.gaussian_process.get_hyperparameters() cov_d = cov_d + np.diag(cov_noise.detach().numpy()) else: raise NotImplementedError('gp_approx method is incorrect or not implemented') # Loop through input constraints and tighten by the required ammount. for ui, input_constraint in enumerate(self.constraints.input_constraints): - input_constraint_set[ui][:, i] = -1*self.inverse_cdf * \ - np.absolute(input_constraint.A) @ np.sqrt(np.diag(cov_u)) + input_constraint_set[ui][:, i] = -1 * self.inverse_cdf * \ + np.absolute(input_constraint.A) @ np.sqrt(np.diag(cov_u)) for si, state_constraint in enumerate(self.constraints.state_constraints): - state_constraint_set[si][:, i] = -1*self.inverse_cdf * \ - np.absolute(state_constraint.A) @ np.sqrt(np.diag(cov_x)) + state_constraint_set[si][:, i] = -1 * self.inverse_cdf * \ + np.absolute(state_constraint.A) @ np.sqrt(np.diag(cov_x)) if self.gp_approx == 'taylor': - raise NotImplementedError("Taylor GP rollout not implemented.") + raise NotImplementedError('Taylor GP rollout not implemented.') elif self.gp_approx == 'mean_eq': # Compute the next step propogated state covariance using mean equivilence. cov_x = self.discrete_dfdx @ cov_x @ self.discrete_dfdx.T + \ - self.discrete_dfdx @ cov_xu @ self.discrete_dfdu.T + \ - self.discrete_dfdu @ cov_xu.T @ self.discrete_dfdx.T + \ - self.discrete_dfdu @ cov_u @ self.discrete_dfdu.T + \ - self.Bd @ cov_d @ self.Bd.T + self.discrete_dfdx @ cov_xu @ self.discrete_dfdu.T + \ + self.discrete_dfdu @ cov_xu.T @ self.discrete_dfdx.T + \ + self.discrete_dfdu @ cov_u @ self.discrete_dfdu.T + \ + self.Bd @ cov_d @ self.Bd.T else: raise NotImplementedError('gp_approx method is incorrect or not implemented') # Udate Final covariance. for si, state_constraint in enumerate(self.constraints.state_constraints): - state_constraint_set[si][:,-1] = -1 * self.inverse_cdf * \ - np.absolute(state_constraint.A) @ np.sqrt(np.diag(cov_x)) + state_constraint_set[si][:, -1] = -1 * self.inverse_cdf * \ + np.absolute(state_constraint.A) @ np.sqrt(np.diag(cov_x)) state_covariances[-1] = cov_x if print_sets: - print("Probabilistic State Constraint values along Horizon:") + print('Probabilistic State Constraint values along Horizon:') print(state_constraint_set) - print("Probabilistic Input Constraint values along Horizon:") + print('Probabilistic Input Constraint values along Horizon:') print(input_constraint_set) self.results_dict['input_constraint_set'].append(input_constraint_set) self.results_dict['state_constraint_set'].append(state_constraint_set) @@ -357,9 +345,9 @@ def precompute_probabilistic_limits(self, return state_constraint_set, input_constraint_set def precompute_mean_post_factor_all_data(self): - """If the number of data points is less than the number of inducing points, use all the data + '''If the number of data points is less than the number of inducing points, use all the data as kernel points. - """ + ''' dim_gp_outputs = len(self.target_mask) n_training_samples = self.train_data['train_targets'].shape[0] inputs = self.train_data['train_inputs'] @@ -367,67 +355,65 @@ def precompute_mean_post_factor_all_data(self): mean_post_factor = np.zeros((dim_gp_outputs, n_training_samples)) for i in range(dim_gp_outputs): K_z_z = self.gaussian_process.K_plus_noise_inv[i] - mean_post_factor[i] = K_z_z.detach().numpy() @ targets[:,self.target_mask[i]] + mean_post_factor[i] = K_z_z.detach().numpy() @ targets[:, self.target_mask[i]] return mean_post_factor, inputs[:, self.input_mask] def precompute_sparse_gp_values(self, n_ind_points): - """Uses the last MPC solution to precomupte values associated with the FITC GP approximation. + '''Uses the last MPC solution to precomupte values associated with the FITC GP approximation. Args: n_ind_points (int): Number of inducing points. - """ + ''' n_data_points = self.gaussian_process.n_training_samples - dim_gp_inputs = len(self.input_mask) dim_gp_outputs = len(self.target_mask) inputs = self.train_data['train_inputs'] targets = self.train_data['train_targets'] # Get the inducing points. if False and self.x_prev is not None and self.u_prev is not None: # Use the previous MPC solution as in Hewing 2019. - z_prev = np.hstack((self.x_prev[:,:-1].T, self.u_prev.T)) - z_prev = z_prev[:,self.input_mask] - inds = self.env.np_random.choice(range(n_data_points), size=n_ind_points-self.T, replace=False) - #z_ind = self.data_inputs[inds][:, self.input_mask] - z_ind = np.vstack((z_prev,inputs[inds][:, self.input_mask])) + z_prev = np.hstack((self.x_prev[:, :-1].T, self.u_prev.T)) + z_prev = z_prev[:, self.input_mask] + inds = self.env.np_random.choice(range(n_data_points), size=n_ind_points - self.T, replace=False) + # z_ind = self.data_inputs[inds][:, self.input_mask] + z_ind = np.vstack((z_prev, inputs[inds][:, self.input_mask])) else: # If there is no previous solution. Choose T random training set points. if self.inducing_point_selection_method == 'kmeans': centroids = kmeans_centriods(n_ind_points, inputs[:, self.input_mask], rand_state=self.seed) - contiguous_masked_inputs = np.ascontiguousarray(inputs[:, self.input_mask]) # required for version sklearn later than 1.0.2 - inds, dist_mat = pairwise_distances_argmin_min(centroids, contiguous_masked_inputs) + contiguous_masked_inputs = np.ascontiguousarray(inputs[:, self.input_mask]) # required for version sklearn later than 1.0.2 + inds, _ = pairwise_distances_argmin_min(centroids, contiguous_masked_inputs) z_ind = inputs[inds][:, self.input_mask] elif self.inducing_point_selection_method == 'random': inds = self.env.np_random.choice(range(n_data_points), size=n_ind_points, replace=False) z_ind = inputs[inds][:, self.input_mask] else: - raise ValueError("[Error]: gp_mpc.precompute_sparse_gp_values: Only 'kmeans' or 'random' allowed.") + raise ValueError('[Error]: gp_mpc.precompute_sparse_gp_values: Only \'kmeans\' or \'random\' allowed.') K_zind_zind = self.gaussian_process.kernel(torch.Tensor(z_ind).double()) K_zind_zind_inv = self.gaussian_process.kernel_inv(torch.Tensor(z_ind).double()) K_x_zind = self.gaussian_process.kernel(torch.from_numpy(inputs[:, self.input_mask]).double(), torch.tensor(z_ind).double()) - #Q_X_X = K_x_zind @ K_zind_zind_inv @ K_x_zind.transpose(1,2) - Q_X_X = K_x_zind @ torch.linalg.solve(K_zind_zind, K_x_zind.transpose(1,2)) + # Q_X_X = K_x_zind @ K_zind_zind_inv @ K_x_zind.transpose(1,2) + Q_X_X = K_x_zind @ torch.linalg.solve(K_zind_zind, K_x_zind.transpose(1, 2)) Gamma = torch.diagonal(self.gaussian_process.K_plus_noise - Q_X_X, 0, 1, 2) - Gamma_inv = torch.diag_embed(1/Gamma) - # Todo: Should inverse be used here instead? pinverse was more stable previsouly. - Sigma_inv = K_zind_zind + K_x_zind.transpose(1,2) @ Gamma_inv @ K_x_zind - Sigma = torch.pinverse(K_zind_zind + K_x_zind.transpose(1,2) @ Gamma_inv @ K_x_zind) + Gamma_inv = torch.diag_embed(1 / Gamma) + # TODO: Should inverse be used here instead? pinverse was more stable previsouly. + Sigma_inv = K_zind_zind + K_x_zind.transpose(1, 2) @ Gamma_inv @ K_x_zind + # Sigma = torch.pinverse(K_zind_zind + K_x_zind.transpose(1, 2) @ Gamma_inv @ K_x_zind) # For debugging mean_post_factor = torch.zeros((dim_gp_outputs, n_ind_points)) for i in range(dim_gp_outputs): mean_post_factor[i] = torch.linalg.solve(Sigma_inv[i], K_x_zind[i].T @ Gamma_inv[i] @ - torch.from_numpy(targets[:,self.target_mask[i]]).double()) - #mean_post_factor[i] = Sigma[i] @ K_x_zind[i].T @ Gamma_inv[i] @ torch.from_numpy(targets[:, self.target_mask[i]]).double() + torch.from_numpy(targets[:, self.target_mask[i]]).double()) + # mean_post_factor[i] = Sigma[i] @ K_x_zind[i].T @ Gamma_inv[i] @ torch.from_numpy(targets[:, self.target_mask[i]]).double() return mean_post_factor.detach().numpy(), Sigma_inv.detach().numpy(), K_zind_zind_inv.detach().numpy(), z_ind - #return mean_post_factor.detach().numpy(), Sigma.detach().numpy(), K_zind_zind_inv.detach().numpy(), z_ind + # return mean_post_factor.detach().numpy(), Sigma.detach().numpy(), K_zind_zind_inv.detach().numpy(), z_ind def setup_gp_optimizer(self, n_ind_points): - """Sets up nonlinear optimization problem including cost objective, variable bounds and dynamics constraints. + '''Sets up nonlinear optimization problem including cost objective, variable bounds and dynamics constraints. Args: n_ind_points (int): Number of inducing points. - - """ + ''' nx, nu = self.model.nx, self.model.nu T = self.T # Define optimizer and variables. @@ -444,7 +430,7 @@ def setup_gp_optimizer(self, n_ind_points): if self.gp_soft_constraints: state_slack_list = [] for state_constraint in self.constraints.state_constraints: - state_slack_list.append(opti.variable(state_constraint.num_constraints, T+1)) + state_slack_list.append(opti.variable(state_constraint.num_constraints, T + 1)) input_slack_list = [] for input_constraint in self.constraints.input_constraints: input_slack_list.append(opti.variable(input_constraint.num_constraints, T)) @@ -452,7 +438,7 @@ def setup_gp_optimizer(self, n_ind_points): # Chance constraint limits. state_constraint_set = [] for state_constraint in self.constraints.state_constraints: - state_constraint_set.append(opti.parameter(state_constraint.num_constraints, T+1)) + state_constraint_set.append(opti.parameter(state_constraint.num_constraints, T + 1)) input_constraint_set = [] for input_constraint in self.constraints.input_constraints: input_constraint_set.append(opti.parameter(input_constraint.num_constraints, T)) @@ -470,16 +456,16 @@ def setup_gp_optimizer(self, n_ind_points): Xr=x_ref[:, i], Ur=np.zeros((nu, 1)), Q=self.Q, - R=self.R)["l"] + R=self.R)['l'] # Terminal cost. cost += cost_func(x=x_var[:, -1], u=np.zeros((nu, 1)), Xr=x_ref[:, -1], Ur=np.zeros((nu, 1)), Q=self.Q, - R=self.R)["l"] - z = cs.vertcat(x_var[:,:-1], u_var) - z = z[self.input_mask,:] + R=self.R)['l'] + z = cs.vertcat(x_var[:, :-1], u_var) + z = z[self.input_mask, :] # Constraints for i in range(self.T): @@ -487,40 +473,40 @@ def setup_gp_optimizer(self, n_ind_points): # This follows the tractable dynamics formulation in Section III.B in Hewing 2019. # Note that for the GP approximation, we are purposely using elementwise multiplication *. if True and self.sparse_gp: - next_state = self.prior_dynamics_func(x0=x_var[:, i]-self.prior_ctrl.X_EQ[:,None], - p=u_var[:, i]-self.prior_ctrl.U_EQ[:,None])['xf'] + \ - self.prior_ctrl.X_EQ[:,None]+ self.Bd @ cs.sum2(self.K_z_zind_func(z1=z[:,i].T, z2=z_ind)['K'] * mean_post_factor) + next_state = self.prior_dynamics_func(x0=x_var[:, i] - self.prior_ctrl.X_EQ[:, None], + p=u_var[:, i] - self.prior_ctrl.U_EQ[:, None])['xf'] + \ + self.prior_ctrl.X_EQ[:, None] + self.Bd @ cs.sum2(self.K_z_zind_func(z1=z[:, i].T, z2=z_ind)['K'] * mean_post_factor) else: # Sparse GP approximation doesn't always work well, thus, use Exact GP regression. This is much slower, # but for unstable systems, make performance much better. - next_state = self.prior_dynamics_func(x0=x_var[:, i]-self.prior_ctrl.X_EQ[:,None], - p=u_var[:, i]-self.prior_ctrl.U_EQ[:,None])['xf'] + \ - self.prior_ctrl.X_EQ[:,None]+ self.Bd @ self.gaussian_process.casadi_predict(z=z[:,i])['mean'] + next_state = self.prior_dynamics_func(x0=x_var[:, i] - self.prior_ctrl.X_EQ[:, None], + p=u_var[:, i] - self.prior_ctrl.U_EQ[:, None])['xf'] + \ + self.prior_ctrl.X_EQ[:, None] + self.Bd @ self.gaussian_process.casadi_predict(z=z[:, i])['mean'] opti.subject_to(x_var[:, i + 1] == next_state) # Probabilistic state and input constraints according to Hewing 2019 constraint tightening. for s_i, state_constraint in enumerate(self.state_constraints_sym): if self.gp_soft_constraints: - opti.subject_to(state_constraint(x_var[:, i]) <= state_constraint_set[s_i][:,i] + state_slack_list[s_i][:,i]) - cost += soft_con_coeff*state_slack_list[s_i][:,i].T @ state_slack_list[s_i][:,i] - opti.subject_to(state_slack_list[s_i][:,i] >= 0) + opti.subject_to(state_constraint(x_var[:, i]) <= state_constraint_set[s_i][:, i] + state_slack_list[s_i][:, i]) + cost += soft_con_coeff * state_slack_list[s_i][:, i].T @ state_slack_list[s_i][:, i] + opti.subject_to(state_slack_list[s_i][:, i] >= 0) else: - opti.subject_to(state_constraint(x_var[:, i]) <= state_constraint_set[s_i][:,i] - self.constraint_tol) + opti.subject_to(state_constraint(x_var[:, i]) <= state_constraint_set[s_i][:, i] - self.constraint_tol) for u_i, input_constraint in enumerate(self.input_constraints_sym): if self.gp_soft_constraints: - opti.subject_to(input_constraint(u_var[:, i]) <= input_constraint_set[u_i][:,i] + input_slack_list[u_i][:, i]) - cost += soft_con_coeff*input_slack_list[u_i][:,i].T @ input_slack_list[u_i][:,i] - opti.subject_to(input_slack_list[u_i][:,i] >= 0) + opti.subject_to(input_constraint(u_var[:, i]) <= input_constraint_set[u_i][:, i] + input_slack_list[u_i][:, i]) + cost += soft_con_coeff * input_slack_list[u_i][:, i].T @ input_slack_list[u_i][:, i] + opti.subject_to(input_slack_list[u_i][:, i] >= 0) else: opti.subject_to(input_constraint(u_var[:, i]) <= input_constraint_set[u_i][:, i] - self.constraint_tol) # Final state constraints. for s_i, state_constraint in enumerate(self.state_constraints_sym): if self.gp_soft_constraints: - opti.subject_to(state_constraint(x_var[:, -1]) <= state_constraint_set[s_i][:, -1] + state_slack_list[s_i][:,-1]) - cost += soft_con_coeff*state_slack_list[s_i][:,-1].T @ state_slack_list[s_i][:,-1] - opti.subject_to(state_slack_list[s_i][:,-1] >= 0) + opti.subject_to(state_constraint(x_var[:, -1]) <= state_constraint_set[s_i][:, -1] + state_slack_list[s_i][:, -1]) + cost += soft_con_coeff * state_slack_list[s_i][:, -1].T @ state_slack_list[s_i][:, -1] + opti.subject_to(state_slack_list[s_i][:, -1] >= 0) else: - opti.subject_to(state_constraint(x_var[:, -1]) <= state_constraint_set[s_i][:,-1] - self.constraint_tol) + opti.subject_to(state_constraint(x_var[:, -1]) <= state_constraint_set[s_i][:, -1] - self.constraint_tol) # Bound constraints. upper_state_bounds = np.clip(self.prior_ctrl.env.observation_space.high, None, 10) @@ -529,82 +515,78 @@ def setup_gp_optimizer(self, n_ind_points): lower_input_bounds = np.clip(self.prior_ctrl.env.action_space.low, -10, None) for i in range(self.T): opti.subject_to(opti.bounded(lower_state_bounds + self.constraint_tol, - x_var[:,i], - upper_state_bounds - self.constraint_tol )) + x_var[:, i], + upper_state_bounds - self.constraint_tol)) opti.subject_to(opti.bounded(lower_input_bounds + self.constraint_tol, - u_var[:,i], + u_var[:, i], upper_input_bounds - self.constraint_tol)) opti.subject_to(opti.bounded(lower_state_bounds + self.constraint_tol, - x_var[:,-1], + x_var[:, -1], upper_state_bounds - self.constraint_tol)) opti.minimize(cost) # Initial condition constraints. opti.subject_to(x_var[:, 0] == x_init) # Create solver (IPOPT solver in this version). - opts = {"ipopt.print_level": 4, - "ipopt.sb": "yes", - "ipopt.max_iter": 100, #100, - "print_time": 1, - "expand": True, - "verbose": True} + opts = {'ipopt.print_level': 4, + 'ipopt.sb': 'yes', + 'ipopt.max_iter': 100, # 100, + 'print_time': 1, + 'expand': True, + 'verbose': True} opti.solver('ipopt', opts) self.opti_dict = { - "opti": opti, - "x_var": x_var, - "u_var": u_var, - "x_init": x_init, - "x_ref": x_ref, - "state_constraint_set": state_constraint_set, - "input_constraint_set": input_constraint_set, - "mean_post_factor": mean_post_factor, - "z_ind": z_ind, - "cost": cost, - "n_ind_points": n_ind_points + 'opti': opti, + 'x_var': x_var, + 'u_var': u_var, + 'x_init': x_init, + 'x_ref': x_ref, + 'state_constraint_set': state_constraint_set, + 'input_constraint_set': input_constraint_set, + 'mean_post_factor': mean_post_factor, + 'z_ind': z_ind, + 'cost': cost, + 'n_ind_points': n_ind_points } - #if False and n_ind_points < self.n_ind_points: + # if False and n_ind_points < self.n_ind_points: if not self.sparse_gp: mean_post_factor_val, z_ind_val = self.precompute_mean_post_factor_all_data() self.mean_post_factor_val = mean_post_factor_val self.z_ind_val = z_ind_val else: - mean_post_factor_val, Sigma, K_zind_zind_inv, z_ind_val = self.precompute_sparse_gp_values(n_ind_points) + mean_post_factor_val, _, _, z_ind_val = self.precompute_sparse_gp_values(n_ind_points) self.mean_post_factor_val = mean_post_factor_val - #self.Sigma = Sigma - #self.K_zind_zind_inv = K_zind_zind_inv self.z_ind_val = z_ind_val def select_action_with_gp(self, obs ): - """Solves nonlinear MPC problem to get next action. + '''Solves nonlinear MPC problem to get next action. Args: obs (np.array): current state/observation. Returns: np.array: input/action to the task/env. - - """ + ''' opti_dict = self.opti_dict - opti = opti_dict["opti"] - x_var = opti_dict["x_var"] - u_var = opti_dict["u_var"] - x_init = opti_dict["x_init"] - x_ref = opti_dict["x_ref"] - state_constraint_set = opti_dict["state_constraint_set"] - input_constraint_set = opti_dict["input_constraint_set"] - mean_post_factor = opti_dict["mean_post_factor"] - z_ind = opti_dict["z_ind"] - cost = opti_dict["cost"] - n_ind_points = opti_dict["n_ind_points"] + opti = opti_dict['opti'] + x_var = opti_dict['x_var'] + u_var = opti_dict['u_var'] + x_init = opti_dict['x_init'] + x_ref = opti_dict['x_ref'] + state_constraint_set = opti_dict['state_constraint_set'] + input_constraint_set = opti_dict['input_constraint_set'] + mean_post_factor = opti_dict['mean_post_factor'] + z_ind = opti_dict['z_ind'] + n_ind_points = opti_dict['n_ind_points'] # Assign the initial state. opti.set_value(x_init, obs) # Assign reference trajectory within horizon. goal_states = self.get_references() opti.set_value(x_ref, goal_states) - if self.mode == "tracking": + if self.mode == 'tracking': self.traj_step += 1 # Set the probabilistic state and input constraint set limits. state_constraint_set_prev, input_constraint_set_prev = self.precompute_probabilistic_limits() @@ -627,6 +609,7 @@ def select_action_with_gp(self, if self.warmstart and self.x_prev is None and self.u_prev is None: x_guess, u_guess = self.prior_ctrl.compute_initial_guess(obs, goal_states, self.X_EQ, self.U_EQ) opti.set_initial(x_var, x_guess) + u_guess = np.clip(u_guess, 0.06, 0.26) opti.set_initial(u_var, u_guess) # Initial guess for optimization problem. elif self.warmstart and self.x_prev is not None and self.u_prev is not None: # shift previous solutions by 1 step @@ -648,16 +631,16 @@ def select_action_with_gp(self, self.results_dict['horizon_states'].append(deepcopy(self.x_prev)) self.results_dict['horizon_inputs'].append(deepcopy(self.u_prev)) self.results_dict['t_wall'].append(opti.stats()['t_wall_total']) - zi = np.hstack((x_val[:,0], u_val[:,0])) + zi = np.hstack((x_val[:, 0], u_val[:, 0])) zi = zi[self.input_mask] - gp_contribution = np.sum(self.K_z_zind_func(z1=zi, z2=z_ind_val)['K'].toarray() * mean_post_factor_val,axis=1) - print("GP Mean eq Contribution: %s" % gp_contribution) - zi = np.hstack((x_val[:,0], u_val[:,0])) - pred, _, _ = self.gaussian_process.predict(zi[None,:]) - print("True GP value: %s" % pred.numpy()) - lin_pred = self.prior_dynamics_func(x0=x_val[:,0]-self.prior_ctrl.X_EQ, - p=u_val[:, 0]-self.prior_ctrl.U_EQ)['xf'].toarray() + \ - self.prior_ctrl.X_EQ[:,None] + gp_contribution = np.sum(self.K_z_zind_func(z1=zi, z2=z_ind_val)['K'].toarray() * mean_post_factor_val, axis=1) + print(f'GP Mean eq Contribution: {gp_contribution}') + zi = np.hstack((x_val[:, 0], u_val[:, 0])) + pred, _, _ = self.gaussian_process.predict(zi[None, :]) + print(f'True GP value: {pred.numpy()}') + lin_pred = self.prior_dynamics_func(x0=x_val[:, 0] - self.prior_ctrl.X_EQ, + p=u_val[:, 0] - self.prior_ctrl.U_EQ)['xf'].toarray() + \ + self.prior_ctrl.X_EQ[:, None] self.results_dict['linear_pred'].append(lin_pred) self.results_dict['gp_mean_eq_pred'].append(gp_contribution) self.results_dict['gp_pred'].append(pred.numpy()) @@ -675,7 +658,7 @@ def learn(self, gp_model=None, overwrite_saved_data: bool = None, ): - """Performs GP training. + '''Performs GP training. Args: input_data, target_data (optiona, np.array): data to use for training @@ -684,8 +667,7 @@ def learn(self, Returns: training_results (dict): Dictionary of the training results. - - """ + ''' if gp_model is None: gp_model = self.gp_model_path if overwrite_saved_data is None: @@ -706,22 +688,22 @@ def learn(self, ############ # Use Latin Hypercube Sampling to generate states withing environment bounds. lhs_sampler = Lhs(lhs_type='classic', criterion='maximin') - #limits = [(self.env.INIT_STATE_RAND_INFO[key].low, self.env.INIT_STATE_RAND_INFO[key].high) for key in + # limits = [(self.env.INIT_STATE_RAND_INFO[key].low, self.env.INIT_STATE_RAND_INFO[key].high) for key in # self.env.INIT_STATE_RAND_INFO] - limits = [(self.env.INIT_STATE_RAND_INFO['init_'+ key]['low'], self.env.INIT_STATE_RAND_INFO['init_' + key]['high']) for key in self.env.STATE_LABELS] - # todo: parameterize this if we actually want it. + limits = [(self.env.INIT_STATE_RAND_INFO['init_' + key]['low'], self.env.INIT_STATE_RAND_INFO['init_' + key]['high']) for key in self.env.STATE_LABELS] + # TODO: parameterize this if we actually want it. num_eq_samples = 0 validation_iterations = int(self.train_iterations * (self.test_data_ratio / (1 - self.test_data_ratio))) samples = lhs_sampler.generate(limits, self.train_iterations + validation_iterations - num_eq_samples, random_state=self.seed) if self.env.TASK == Task.STABILIZATION and num_eq_samples > 0: - # todo: choose if we want eq samples or not. + # TODO: choose if we want eq samples or not. delta_plus = np.array([0.1, 0.1, 0.1, 0.1, 0.03, 0.3]) delta_neg = np.array([0.1, 0.1, 0.1, 0.1, 0.03, 0.3]) - eq_limits = [(self.prior_ctrl.env.X_GOAL[eq]-delta_neg[eq], self.prior_ctrl.env.X_GOAL[eq]+delta_plus[eq]) for eq in range(self.model.nx)] + eq_limits = [(self.prior_ctrl.env.X_GOAL[eq] - delta_neg[eq], self.prior_ctrl.env.X_GOAL[eq] + delta_plus[eq]) for eq in range(self.model.nx)] eq_samples = lhs_sampler.generate(eq_limits, num_eq_samples, random_state=self.seed) - #samples = samples.append(eq_samples) + # samples = samples.append(eq_samples) init_state_samples = np.array(samples + eq_samples) else: init_state_samples = np.array(samples) @@ -730,21 +712,21 @@ def learn(self, input_samples = lhs_sampler.generate(input_limits, self.train_iterations + validation_iterations, random_state=self.seed) - input_samples = np.array(input_samples) # not being used currently - seeds = self.env.np_random.randint(0,99999, size=self.train_iterations + validation_iterations) + input_samples = np.array(input_samples) # not being used currently + seeds = self.env.np_random.integers(0, 99999, size=self.train_iterations + validation_iterations) for i in range(self.train_iterations + validation_iterations): # For random initial state training. - #init_state = init_state_samples[i,:] + # init_state = init_state_samples[i,:] init_state = dict(zip(self.env.INIT_STATE_RAND_INFO.keys(), init_state_samples[i, :])) # Collect data with prior controller. run_env = self.env_func(init_state=init_state, randomized_init=False, seed=int(seeds[i])) episode_results = self.prior_ctrl.run(env=run_env, max_steps=1) run_env.close() - x_obs = episode_results['obs'][-3:,:] - u_seq = episode_results['action'][-1:,:] + x_obs = episode_results['obs'][-3:, :] + u_seq = episode_results['action'][-1:, :] run_env.close() - x_seq = x_obs[:-1,:] - x_next_seq = x_obs[1:,:] + x_seq = x_obs[:-1, :] + x_next_seq = x_obs[1:, :] train_inputs_i, train_targets_i = self.preprocess_training_data(x_seq, u_seq, x_next_seq) train_inputs.append(train_inputs_i) train_targets.append(train_targets_i) @@ -762,16 +744,16 @@ def learn(self, self.data_inputs = np.vstack((self.data_inputs, train_inputs)) self.data_targets = np.vstack((self.data_targets, train_targets)) else: - raise ValueError("[ERROR]: gp_mpc.learn(): Need to provide both targets and inputs.") + raise ValueError('[ERROR]: gp_mpc.learn(): Need to provide both targets and inputs.') total_input_data = self.data_inputs.shape[0] # If validation set is desired. if self.test_data_ratio > 0 and self.test_data_ratio is not None: train_idx, test_idx = train_test_split( - list(range(total_input_data)), - test_size=self.test_data_ratio, - random_state=self.seed - ) + list(range(total_input_data)), + test_size=self.test_data_ratio, + random_state=self.seed + ) else: # Otherwise, just copy the training data into the test data. @@ -795,12 +777,12 @@ def learn(self, noise_constraint=gpytorch.constraints.GreaterThan(1e-6), ).double() self.gaussian_process = GaussianProcessCollection(ZeroMeanIndependentGPModel, - likelihood, - len(self.target_mask), - input_mask=self.input_mask, - target_mask=self.target_mask, - normalize=self.normalize_training_data - ) + likelihood, + len(self.target_mask), + input_mask=self.input_mask, + target_mask=self.target_mask, + normalize=self.normalize_training_data + ) if gp_model: self.gaussian_process.init_with_hyperparam(train_inputs_tensor, train_targets_tensor, @@ -814,15 +796,15 @@ def learn(self, n_train=self.optimization_iterations, learning_rate=self.learning_rate, gpu=self.use_gpu, - dir=self.output_dir) + output_dir=self.output_dir) self.reset() - #if self.train_data['train_targets'].shape[0] <= self.n_ind_points: + # if self.train_data['train_targets'].shape[0] <= self.n_ind_points: # n_ind_points = self.train_data['train_targets'].shape[0] - #else: + # else: # n_ind_points = self.n_ind_points - #self.set_gp_dynamics_func(n_ind_points) - #self.setup_gp_optimizer(n_ind_points) + # self.set_gp_dynamics_func(n_ind_points) + # self.setup_gp_optimizer(n_ind_points) self.prior_ctrl.add_constraints(self.prior_ctrl.additional_constraints) self.prior_ctrl.reset() # Collect training results. @@ -836,44 +818,41 @@ def learn(self, return training_results def select_action(self, - obs + obs, + info=None, ): - """Select the action based on the given observation. + '''Select the action based on the given observation. Args: - obs (np.array): current observed state. + obs (ndarray): Current observed state. + info (dict): Current info. Returns: - action (np.array): desired policy action. + action (ndarray): Desired policy action. + ''' - """ if self.gaussian_process is None: action = self.prior_ctrl.select_action(obs) else: - if(self.last_obs is not None and self.last_action is not None and self.online_learning): - print("[ERROR]: Not yet supported.") + if (self.last_obs is not None and self.last_action is not None and self.online_learning): + print('[ERROR]: Not yet supported.') exit() t1 = time.perf_counter() action = self.select_action_with_gp(obs) t2 = time.perf_counter() - print("GP SELECT ACTION TIME: %s" %(t2 - t1)) + print(f'GP SELECT ACTION TIME: {(t2 - t1)}') self.last_obs = obs self.last_action = action return action def close(self): - """Clean up. - - """ + '''Clean up.''' self.env_training.close() self.env.close() - def reset_results_dict(self): - """ - - """ - "Result the results_dict before running." - super().reset_results_dict() + def setup_results_dict(self): + '''Setup the results dictionary to store run information.''' + super().setup_results_dict() self.results_dict['input_constraint_set'] = [] self.results_dict['state_constraint_set'] = [] self.results_dict['state_horizon_cov'] = [] @@ -885,15 +864,13 @@ def reset_results_dict(self): self.results_dict['inducing_points'] = [] def reset(self): - """Reset the controller before running. - - """ + '''Reset the controller before running.''' # Setup reference input. if self.env.TASK == Task.STABILIZATION: - self.mode = "stabilization" + self.mode = 'stabilization' self.x_goal = self.env.X_GOAL elif self.env.TASK == Task.TRAJ_TRACKING: - self.mode = "tracking" + self.mode = 'tracking' self.traj = self.env.X_GOAL.T self.traj_step = 0 # Dynamics model. @@ -908,6 +885,7 @@ def reset(self): self.set_gp_dynamics_func(n_ind_points) self.setup_gp_optimizer(n_ind_points) self.prior_ctrl.reset() + self.setup_results_dict() # Previously solved states & inputs, useful for warm start. self.x_prev = None self.u_prev = None diff --git a/safe_control_gym/controllers/mpc/gp_mpc.yaml b/safe_control_gym/controllers/mpc/gp_mpc.yaml index 3e8e36023..ac0bfd118 100644 --- a/safe_control_gym/controllers/mpc/gp_mpc.yaml +++ b/safe_control_gym/controllers/mpc/gp_mpc.yaml @@ -22,8 +22,7 @@ gp_approx: mean_eq online_learning: False # Prior Model args -inertial_prop: - - 1.0 +prior_info: {} prior_param_coeff: 1. # Runner args diff --git a/safe_control_gym/controllers/mpc/gp_utils.py b/safe_control_gym/controllers/mpc/gp_utils.py index 61fc3e860..be7006d6b 100644 --- a/safe_control_gym/controllers/mpc/gp_utils.py +++ b/safe_control_gym/controllers/mpc/gp_utils.py @@ -1,25 +1,27 @@ -"""Utility functions for Gaussian Processes. +'''Utility functions for Gaussian Processes.''' -""" import os.path -import numpy as np +from copy import deepcopy + +import casadi as ca import gpytorch -import torch import matplotlib.pyplot as plt -import casadi as ca -from copy import deepcopy +import numpy as np +import torch from sklearn import preprocessing from sklearn.cluster import KMeans from safe_control_gym.utils.utils import mkdirs + torch.manual_seed(0) + def covSEard(x, z, ell, sf2 ): - """GP squared exponential kernel. + '''GP squared exponential kernel. This function is based on the 2018 GP-MPC library by Helge-André Langåker @@ -31,18 +33,16 @@ def covSEard(x, Returns: SE kernel (casadi.MX/SX): SE kernel. - - """ + ''' dist = ca.sum1((x - z)**2 / ell**2) return sf2 * ca.SX.exp(-.5 * dist) class ZeroMeanIndependentMultitaskGPModel(gpytorch.models.ExactGP): - """Multidimensional Gaussian Process model with zero mean function. + '''Multidimensional Gaussian Process model with zero mean function. Or constant mean and radial basis function kernel (SE). - - """ + ''' def __init__(self, train_x, @@ -50,15 +50,14 @@ def __init__(self, likelihood, nx ): - """Initialize a multidimensional Gaussian Process model with zero mean function. + '''Initialize a multidimensional Gaussian Process model with zero mean function. Args: train_x (torch.Tensor): input training data (input_dim X N samples). train_y (torch.Tensor): output training data (output dim x N samples). likelihood (gpytorch.likelihood): Likelihood function (gpytorch.likelihoods.MultitaskGaussianLikelihood). nx (int): dimension of the target output (output dim) - - """ + ''' super().__init__(train_x, train_y, likelihood) self.n = nx # For Zero mean function. @@ -75,9 +74,6 @@ def __init__(self, def forward(self, x ): - """ - - """ mean_x = self.mean_module(x) covar_x = self.covar_module(x) return gpytorch.distributions.MultitaskMultivariateNormal.from_batch_mvn( @@ -86,24 +82,23 @@ def forward(self, class ZeroMeanIndependentGPModel(gpytorch.models.ExactGP): - """Single dimensional output Gaussian Process model with zero mean function. + '''Single dimensional output Gaussian Process model with zero mean function. Or constant mean and radial basis function kernel (SE). + ''' - """ def __init__(self, train_x, train_y, likelihood ): - """Initialize a single dimensional Gaussian Process model with zero mean function. + '''Initialize a single dimensional Gaussian Process model with zero mean function. Args: train_x (torch.Tensor): input training data (input_dim X N samples). train_y (torch.Tensor): output training data (output dim x N samples). likelihood (gpytorch.likelihood): Likelihood function (gpytorch.likelihoods.GaussianLikelihood). - - """ + ''' super().__init__(train_x, train_y, likelihood) # For Zero mean function. self.mean_module = gpytorch.means.ZeroMean() @@ -116,18 +111,13 @@ def __init__(self, def forward(self, x ): - """ - - """ mean_x = self.mean_module(x) covar_x = self.covar_module(x) return gpytorch.distributions.MultivariateNormal(mean_x, covar_x) class GaussianProcessCollection: - """Collection of GaussianProcesses for multioutput GPs. - - """ + '''Collection of GaussianProcesses for multioutput GPs.''' def __init__(self, model_type, likelihood, @@ -136,7 +126,7 @@ def __init__(self, model_type, target_mask=None, normalize=False ): - """Creates a single GaussianProcess for each output dimension. + '''Creates a single GaussianProcess for each output dimension. Args: model_type (gpytorch model class): Model class for the GP (ZeroMeanIndependentGPModel). @@ -145,8 +135,7 @@ def __init__(self, model_type, input_mask (list): Input dimensions to keep. If None, use all input dimensions. target_mask (list): Target dimensions to keep. If None, use all target dimensions. normalize (bool): If True, scale all data between -1 and 1. - - """ + ''' self.gp_list = [] self.model_type = model_type self.likelihood = likelihood @@ -155,7 +144,7 @@ def __init__(self, model_type, self.NORMALIZE = normalize self.input_mask = input_mask self.target_mask = target_mask - for i in range(target_dim): + for _ in range(target_dim): self.gp_list.append(GaussianProcess(model_type, deepcopy(likelihood), input_mask=input_mask, @@ -165,12 +154,11 @@ def _init_properties(self, train_inputs, train_targets ): - """Initialize useful properties. + '''Initialize useful properties. Args: train_inputs, train_targets (torch.tensors): Input and target training data. - - """ + ''' target_dimension = train_targets.shape[1] self.input_dimension = train_inputs.shape[1] self.output_dimension = target_dimension @@ -181,25 +169,23 @@ def init_with_hyperparam(self, train_targets, path_to_statedicts ): - """Load hyperparameters from a state_dict. + '''Load hyperparameters from a state_dict. Args: train_inputs, train_targets (torch.tensors): Input and target training data. path_to_statedicts (str): Path to where the state dicts are saved. - - """ + ''' self._init_properties(train_inputs, train_targets) - target_dimension = train_targets.shape[1] gp_K_plus_noise_list = [] gp_K_plus_noise_inv_list = [] for gp_ind, gp in enumerate(self.gp_list): - path = os.path.join(path_to_statedicts, 'best_model_%s.pth' % self.target_mask[gp_ind]) - print("#########################################") - print("# Loading GP dimension %s #" % self.target_mask[gp_ind]) - print("#########################################") - print('Path: %s' % path) + path = os.path.join(path_to_statedicts, f'best_model_{self.target_mask[gp_ind]}.pth') + print('#########################################') + print('# Loading GP dimension {self.target_mask[gp_ind]} #') + print('#########################################') + print(f'Path: {path}') gp.init_with_hyperparam(train_inputs, - train_targets[:,self.target_mask[gp_ind]], + train_targets[:, self.target_mask[gp_ind]], path) gp_K_plus_noise_list.append(gp.model.K_plus_noise.detach()) gp_K_plus_noise_inv_list.append(gp.model.K_plus_noise_inv.detach()) @@ -213,9 +199,7 @@ def init_with_hyperparam(self, def get_hyperparameters(self, as_numpy=False ): - """Get the outputscale and lengthscale from the kernel matrices of the GPs. - - """ + '''Get the outputscale and lengthscale from the kernel matrices of the GPs.''' lengthscale_list = [] output_scale_list = [] noise_list = [] @@ -239,37 +223,36 @@ def train(self, n_train=[500], learning_rate=[0.01], gpu=False, - dir='results' + output_dir='results' ): - """Train the GP using Train_x and Train_y. + '''Train the GP using Train_x and Train_y. Args: train_x: Torch tensor (N samples [rows] by input dim [cols]) train_y: Torch tensor (N samples [rows] by target dim [cols]) - - """ + ''' self._init_properties(train_x_raw, train_y_raw) self.model_paths = [] - mkdirs(dir) + mkdirs(output_dir) gp_K_plus_noise_inv_list = [] gp_K_plus_noise_list = [] for gp_ind, gp in enumerate(self.gp_list): lr = learning_rate[self.target_mask[gp_ind]] n_t = n_train[self.target_mask[gp_ind]] - print("#########################################") - print("# Training GP dimension %s #" % self.target_mask[gp_ind]) - print("#########################################") - print("Train iterations: %s" % n_t) - print("Learning Rate:: %s" % lr) + print('#########################################') + print(f'# Training GP dimension {self.target_mask[gp_ind]} #') + print('#########################################') + print(f'Train iterations: {n_t}') + print(f'Learning Rate: {lr}') gp.train(train_x_raw, - train_y_raw[:,self.target_mask[gp_ind]], + train_y_raw[:, self.target_mask[gp_ind]], test_x_raw, test_y_raw[:, self.target_mask[gp_ind]], n_train=n_t, learning_rate=lr, gpu=gpu, - fname=os.path.join(dir, 'best_model_%s.pth' % self.target_mask[gp_ind])) - self.model_paths.append(dir) + fname=os.path.join(output_dir, f'best_model_{self.target_mask[gp_ind]}.pth')) + self.model_paths.append(output_dir) gp_K_plus_noise_list.append(gp.model.K_plus_noise) gp_K_plus_noise_inv_list.append(gp.model.K_plus_noise_inv) gp_K_plus_noise = torch.stack(gp_K_plus_noise_list) @@ -278,14 +261,12 @@ def train(self, self.K_plus_noise_inv = gp_K_plus_noise_inv self.casadi_predict = self.make_casadi_predict_func() - def predict(self, x, requires_grad=False, return_pred=True ): - """ - + ''' Args: x : torch.Tensor (N_samples x input DIM). @@ -294,8 +275,7 @@ def predict(self, mean : torch.tensor (nx X N_samples). lower : torch.tensor (nx X N_samples). upper : torch.tensor (nx X N_samples). - - """ + ''' means_list = [] cov_list = [] pred_list = [] @@ -315,11 +295,10 @@ def predict(self, return means, cov def make_casadi_predict_func(self): - """ + ''' Assume train_inputs and train_tergets are already - """ + ''' - means_list = [] Nz = len(self.input_mask) Ny = len(self.target_mask) z = ca.SX.sym('z1', Nz) @@ -333,14 +312,10 @@ def make_casadi_predict_func(self): ['mean']) return casadi_predict - - def prediction_jacobian(self, query ): - """Return Jacobian. - - """ + '''Return Jacobian.''' raise NotImplementedError def plot_trained_gp(self, @@ -348,12 +323,10 @@ def plot_trained_gp(self, targets, fig_count=0 ): - """Plot the trained GP given the input and target data. - - """ + '''Plot the trained GP given the input and target data.''' for gp_ind, gp in enumerate(self.gp_list): fig_count = gp.plot_trained_gp(inputs, - targets[:,self.target_mask[gp_ind],None], + targets[:, self.target_mask[gp_ind], None], self.target_mask[gp_ind], fig_count=fig_count) fig_count += 1 @@ -362,7 +335,7 @@ def _kernel_list(self, x1, x2=None ): - """Evaluate the kernel given vectors x1 and x2. + '''Evaluate the kernel given vectors x1 and x2. Args: x1 (torch.Tensor): First vector. @@ -370,12 +343,11 @@ def _kernel_list(self, Returns: list of LazyTensor Kernels. - - """ + ''' if x2 is None: x2 = x1 - # todo: Make normalization at the GPCollection level? - #if self.NORMALIZE: + # TODO: Make normalization at the GPCollection level? + # if self.NORMALIZE: # x1 = torch.from_numpy(self.gp_list[0].scaler.transform(x1.numpy())) # x2 = torch.from_numpy(self.gp_list[0].scaler.transform(x2.numpy())) k_list = [] @@ -387,7 +359,7 @@ def kernel(self, x1, x2=None ): - """Evaluate the kernel given vectors x1 and x2. + '''Evaluate the kernel given vectors x1 and x2. Args: x1 (torch.Tensor): First vector. @@ -395,8 +367,7 @@ def kernel(self, Returns: Torch tensor of the non-lazy kernel matrices. - - """ + ''' k_list = self._kernel_list(x1, x2) non_lazy_tensors = [k.evaluate() for k in k_list] return torch.stack(non_lazy_tensors) @@ -405,7 +376,7 @@ def kernel_inv(self, x1, x2=None ): - """Evaluate the inverse kernel given vectors x1 and x2. + '''Evaluate the inverse kernel given vectors x1 and x2. Only works for square kernel. @@ -415,37 +386,34 @@ def kernel_inv(self, Returns: Torch tensor of the non-lazy inverse kernel matrices. - - """ + ''' if x2 is None: x2 = x1 - assert x1.shape == x2.shape, ValueError("x1 and x2 need to have the same shape.") + assert x1.shape == x2.shape, ValueError('x1 and x2 need to have the same shape.') k_list = self._kernel_list(x1, x2) num_of_points = x1.shape[0] # Efficient inversion is performed VIA inv_matmul on the laze tensor with Identity. non_lazy_tensors = [k.inv_matmul(torch.eye(num_of_points).double()) for k in k_list] return torch.stack(non_lazy_tensors) -class GaussianProcess: - """Gaussian Process decorator for gpytorch. - """ +class GaussianProcess: + '''Gaussian Process decorator for gpytorch.''' def __init__(self, - model_type, - likelihood, - input_mask=None, - target_mask=None, - normalize=False - ): - """Initialize Gaussian Process. - + model_type, + likelihood, + input_mask=None, + target_mask=None, + normalize=False + ): + '''Initialize Gaussian Process. + Args: model_type (gpytorch model class): Model class for the GP (ZeroMeanIndependentMultitaskGPModel). likelihood (gpytorch.likelihood): likelihood function. normalize (bool): If True, scale all data between -1 and 1. (prototype and not fully operational). - - """ + ''' self.model_type = model_type self.likelihood = likelihood self.optimizer = None @@ -458,9 +426,7 @@ def _init_model(self, train_inputs, train_targets ): - """Init GP model from train inputs and train_targets. - - """ + '''Init GP model from train inputs and train_targets.''' if train_targets.ndim > 1: target_dimension = train_targets.shape[1] else: @@ -480,29 +446,24 @@ def _init_model(self, self.output_dimension = target_dimension self.n_training_samples = train_inputs.shape[0] - def _compute_GP_covariances(self, train_x ): - """Compute K(X,X) + sigma*I and its inverse. - - """ + '''Compute K(X,X) + sigma*I and its inverse.''' # Pre-compute inverse covariance plus noise to speed-up computation. K_lazy = self.model.covar_module(train_x.double()) K_lazy_plus_noise = K_lazy.add_diag(self.model.likelihood.noise) n_samples = train_x.shape[0] self.model.K_plus_noise = K_lazy_plus_noise.matmul(torch.eye(n_samples).double()) self.model.K_plus_noise_inv = K_lazy_plus_noise.inv_matmul(torch.eye(n_samples).double()) - #self.model.K_plus_noise_inv_2 = torch.inverse(self.model.K_plus_noise) # Equivalent to above but slower. + # self.model.K_plus_noise_inv_2 = torch.inverse(self.model.K_plus_noise) # Equivalent to above but slower. def init_with_hyperparam(self, train_inputs, train_targets, path_to_statedict ): - """Load hyperparameters from a state_dict. - - """ + '''Load hyperparameters from a state_dict.''' if self.input_mask is not None: train_inputs = train_inputs[:, self.input_mask] if self.target_mask is not None: @@ -513,7 +474,7 @@ def init_with_hyperparam(self, if self.NORMALIZE: train_inputs = torch.from_numpy(self.scaler.transform(train_inputs.numpy())) self.model.load_state_dict(state_dict) - self.model.double() # needed otherwise loads state_dict as float32 + self.model.double() # needed otherwise loads state_dict as float32 self._compute_GP_covariances(train_inputs) self.casadi_predict = self.make_casadi_prediction_func(train_inputs, train_targets) @@ -527,13 +488,12 @@ def train(self, gpu=False, fname='best_model.pth', ): - """Train the GP using Train_x and Train_y. + '''Train the GP using Train_x and Train_y. Args: train_x: Torch tensor (N samples [rows] by input dim [cols]) train_y: Torch tensor (N samples [rows] by target dim [cols]) - - """ + ''' train_x_raw = train_input_data train_y_raw = train_target_data test_x_raw = test_input_data @@ -588,7 +548,7 @@ def train(self, print('Iter %d/%d - MLL trian Loss: %.3f, Posterior Test Loss: %0.3f' % (i + 1, n_train, loss.item(), test_loss.item())) self.optimizer.step() - #if test_loss < best_loss: + # if test_loss < best_loss: # best_loss = test_loss # state_dict = self.model.state_dict() # torch.save(state_dict, fname) @@ -599,10 +559,10 @@ def train(self, torch.save(state_dict, fname) best_epoch = i - i+=1 - print("Training Complete") - print("Lowest epoch: %s" %best_epoch) - print("Lowest Loss: %s" % best_loss) + i += 1 + print('Training Complete') + print(f'Lowest epoch: {best_epoch}') + print(f'Lowest Loss: {best_loss}') self.model = self.model.cpu() self.likelihood = self.likelihood.cpu() train_x = train_x.cpu() @@ -616,8 +576,7 @@ def predict(self, requires_grad=False, return_pred=True ): - """ - + ''' Args: x : torch.Tensor (N_samples x input DIM). @@ -626,14 +585,13 @@ def predict(self, mean : torch.tensor (nx X N_samples). lower : torch.tensor (nx X N_samples). upper : torch.tensor (nx X N_samples). - - """ + ''' self.model.eval() self.likelihood.eval() - if type(x) is np.ndarray: + if isinstance(x, np.ndarray): x = torch.from_numpy(x).double() if self.input_mask is not None: - x = x[:,self.input_mask] + x = x[:, self.input_mask] if self.NORMALIZE: x = torch.from_numpy(self.scaler.transform(x)) if requires_grad: @@ -650,19 +608,16 @@ def predict(self, else: return mean, cov - def prediction_jacobian(self, query ): - mean_der, cov_der = torch.autograd.functional.jacobian( - lambda x: self.predict(x, requires_grad=True, return_pred=False), - query.double()) + mean_der, _ = torch.autograd.functional.jacobian( + lambda x: self.predict(x, requires_grad=True, return_pred=False), + query.double()) return mean_der.detach().squeeze() def make_casadi_prediction_func(self, train_inputs, train_targets): - """ - Assumes train_inputs and train_targets are already masked. - """ + '''Assumes train_inputs and train_targets are already masked.''' train_inputs = train_inputs.numpy() train_targets = train_targets.numpy() lengthscale = self.model.covar_module.base_kernel.lengthscale.detach().numpy() @@ -689,14 +644,14 @@ def plot_trained_gp(self, ): if self.target_mask is not None: targets = targets[:, self.target_mask] - means, covs, preds = self.predict(inputs) + means, _, preds = self.predict(inputs) t = np.arange(inputs.shape[0]) lower, upper = preds.confidence_region() for i in range(self.output_dimension): fig_count += 1 plt.figure(fig_count) if lower.ndim > 1: - plt.fill_between(t, lower[:,i].detach().numpy(), upper[:,i].detach().numpy(), alpha=0.5, label='95%') + plt.fill_between(t, lower[:, i].detach().numpy(), upper[:, i].detach().numpy(), alpha=0.5, label='95%') plt.plot(t, means[:, i], 'r', label='GP Mean') plt.plot(t, targets[:, i], '*k', label='Data') else: @@ -704,14 +659,15 @@ def plot_trained_gp(self, plt.plot(t, means, 'r', label='GP Mean') plt.plot(t, targets, '*k', label='Data') plt.legend() - plt.title('Fitted GP x%s' % output_label) + plt.title(f'Fitted GP x{output_label}') plt.xlabel('Time (s)') plt.ylabel('v') plt.show() return fig_count + def kmeans_centriods(n_cent, data, rand_state=0): - """kmeans clustering. Useful for finding reasonable inducing points. + '''kmeans clustering. Useful for finding reasonable inducing points. Args: n_cent (int): Number of centriods. @@ -719,7 +675,6 @@ def kmeans_centriods(n_cent, data, rand_state=0): Return: centriods (np.array): Array of centriods (n_cent X n_features). - - """ + ''' kmeans = KMeans(n_clusters=n_cent, random_state=rand_state).fit(data) return kmeans.cluster_centers_ diff --git a/safe_control_gym/controllers/mpc/linear_mpc.py b/safe_control_gym/controllers/mpc/linear_mpc.py index 0ac9ffa04..1858faa66 100644 --- a/safe_control_gym/controllers/mpc/linear_mpc.py +++ b/safe_control_gym/controllers/mpc/linear_mpc.py @@ -1,26 +1,25 @@ -"""Linear Model Predictive Control. +'''Linear Model Predictive Control. Based on: * https://people.eecs.berkeley.edu/~pabbeel/cs287-fa12/slides/LQR.pdf - * https://pythonrobotics.readthedocs.io/en/latest/modules/path_tracking.html#mpc-modeling + * https://pythonrobotics.readthedocs.io/en/latest/modules/path_tracking.html#mpc-modeling * https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +''' -""" -import numpy as np -import casadi as cs - -from sys import platform from copy import deepcopy +from sys import platform +import casadi as cs +import numpy as np + +from safe_control_gym.controllers.lqr.lqr_utils import discretize_linear_system from safe_control_gym.controllers.mpc.mpc import MPC -from safe_control_gym.controllers.mpc.mpc_utils import discretize_linear_system, compute_discrete_lqr_gain_from_cont_linear_system +from safe_control_gym.controllers.mpc.mpc_utils import compute_discrete_lqr_gain_from_cont_linear_system from safe_control_gym.envs.benchmark_env import Task class LinearMPC(MPC): - """ Simple linear MPC. - - """ + '''Simple linear MPC.''' def __init__( self, @@ -31,13 +30,14 @@ def __init__( warmstart=True, soft_constraints=False, terminate_run_on_done=True, - constraint_tol: float=1e-8, + constraint_tol: float = 1e-8, + solver: str = 'sqpmethod', # runner args # shared/base args - output_dir="results/temp", + output_dir='results/temp', additional_constraints=None, **kwargs): - """Creates task and controller. + '''Creates task and controller. Args: env_func (Callable): function to instantiate task/environment. @@ -48,14 +48,15 @@ def __init__( soft_constraints (bool): Formulate the constraints as soft constraints. terminate_run_on_done (bool): Terminate the run when the environment returns done or not. constraint_tol (float): Tolerance to add the the constraint as sometimes solvers are not exact. + solver (str): Specify which solver you wish to use (qrqp, qpoases, ipopt, sqpmethod) output_dir (str): output directory to write logs and results. additional_constraints (list): list of constraints. - - """ + ''' # Store all params/args. for k, v in locals().items(): - if k != "self" and k != "kwargs" and "__" not in k: + if k != 'self' and k != 'kwargs' and '__' not in k: self.__dict__[k] = v + super().__init__( env_func, horizon=horizon, @@ -65,38 +66,38 @@ def __init__( soft_constraints=soft_constraints, terminate_run_on_done=terminate_run_on_done, constraint_tol=constraint_tol, + # prior_info=prior_info, output_dir=output_dir, additional_constraints=additional_constraints, **kwargs ) - # todo: setup environment equilibrium - #self.X_EQ = np.atleast_2d(self.env.X_GOAL)[0,:].T - #self.U_EQ = np.atleast_2d(self.env.U_GOAL)[0,:] - - self.X_EQ = np.atleast_2d(self.env.X_EQ)[0,:].T - self.U_EQ = np.atleast_2d(self.env.U_EQ)[0,:].T + # TODO: setup environment equilibrium + # self.X_EQ = np.atleast_2d(self.env.X_GOAL)[0,:].T + # self.U_EQ = np.atleast_2d(self.env.U_GOAL)[0,:] + self.X_EQ = np.atleast_2d(self.model.X_EQ)[0, :].T + self.U_EQ = np.atleast_2d(self.model.U_EQ)[0, :].T + assert solver in ['qpoases', 'qrqp', 'sqpmethod', 'ipopt'], '[Error]. MPC Solver not supported.' + self.solver = solver def set_dynamics_func(self): - """Updates symbolic dynamics with actual control frequency. - - """ + '''Updates symbolic dynamics with actual control frequency.''' # Original version, used in shooting. dfdxdfdu = self.model.df_func(x=self.X_EQ, u=self.U_EQ) dfdx = dfdxdfdu['dfdx'].toarray() dfdu = dfdxdfdu['dfdu'].toarray() - delta_x = cs.MX.sym('delta_x', self.model.nx,1) - delta_u = cs.MX.sym('delta_u', self.model.nu,1) - #x_dot_lin_vec = dfdx @ delta_x + dfdu @ delta_u - #self.linear_dynamics_func = cs.integrator( + delta_x = cs.MX.sym('delta_x', self.model.nx, 1) + delta_u = cs.MX.sym('delta_u', self.model.nu, 1) + # x_dot_lin_vec = dfdx @ delta_x + dfdu @ delta_u + # self.linear_dynamics_func = cs.integrator( # 'linear_discrete_dynamics', self.model.integration_algo, # { # 'x': delta_x, # 'p': delta_u, # 'ode': x_dot_lin_vec # }, {'tf': self.dt} - #) + # ) Ad, Bd = discretize_linear_system(dfdx, dfdu, self.dt, exact=True) x_dot_lin = Ad @ delta_x + Bd @ delta_u self.linear_dynamics_func = cs.Function('linear_discrete_dynamics', @@ -108,33 +109,35 @@ def set_dynamics_func(self): self.dfdu = dfdu def compute_initial_guess(self, init_state, goal_states, x_lin, u_lin): - """Use LQR to get an initial guess of the """ + '''Use LQR to get an initial guess of the ''' dfdxdfdu = self.model.df_func(x=x_lin, u=u_lin) dfdx = dfdxdfdu['dfdx'].toarray() dfdu = dfdxdfdu['dfdu'].toarray() - lqr_gain, Ad, Bd = compute_discrete_lqr_gain_from_cont_linear_system(dfdx, dfdu, self.Q, self.R, self.dt) + lqr_gain, _, _ = compute_discrete_lqr_gain_from_cont_linear_system(dfdx, dfdu, self.Q, self.R, self.dt) - x_guess = np.zeros((self.model.nx, self.T+1)) + x_guess = np.zeros((self.model.nx, self.T + 1)) u_guess = np.zeros((self.model.nu, self.T)) x_guess[:, 0] = init_state for i in range(self.T): u = lqr_gain @ (x_guess[:, i] - goal_states[:, i]) + u_lin u_guess[:, i] = u - x_guess[:, i+1, None] = self.linear_dynamics_func(x0=x_guess[:, i], p=u)['xf'].toarray() + x_guess[:, i + 1, None] = self.linear_dynamics_func(x0=x_guess[:, i], p=u)['xf'].toarray() return x_guess, u_guess def setup_optimizer(self): - """Sets up convex optimization problem. + '''Sets up convex optimization problem. Including cost objective, variable bounds and dynamics constraints. - - """ + ''' nx, nu = self.model.nx, self.model.nu T = self.T # Define optimizer and variables. - opti = cs.Opti() + if self.solver in ['qrqp', 'qpoases']: + opti = cs.Opti('conic') + else: + opti = cs.Opti() # States. x_var = opti.variable(nx, T + 1) # Inputs. @@ -151,22 +154,22 @@ def setup_optimizer(self): cost = 0 cost_func = self.model.loss for i in range(T): - cost += cost_func(x=x_var[:, i]+self.X_EQ[:, None], - u=u_var[:, i]+self.U_EQ[:, None], + cost += cost_func(x=x_var[:, i] + self.X_EQ[:, None], + u=u_var[:, i] + self.U_EQ[:, None], Xr=x_ref[:, i], Ur=np.zeros((nu, 1)), Q=self.Q, - R=self.R)["l"] + R=self.R)['l'] # Terminal cost. - cost += cost_func(x=x_var[:, -1]+self.X_EQ[:,None], - u=np.zeros((nu, 1))+self.U_EQ[:, None], + cost += cost_func(x=x_var[:, -1] + self.X_EQ[:, None], + u=np.zeros((nu, 1)) + self.U_EQ[:, None], Xr=x_ref[:, -1], Ur=np.zeros((nu, 1)), Q=self.Q, - R=self.R)["l"] + R=self.R)['l'] for i in range(self.T): # Dynamics constraints. - next_state = self.linear_dynamics_func(x0=x_var[:, i], p=u_var[:,i])['xf'] + next_state = self.linear_dynamics_func(x0=x_var[:, i], p=u_var[:, i])['xf'] opti.subject_to(x_var[:, i + 1] == next_state) # State and input constraints @@ -174,18 +177,18 @@ def setup_optimizer(self): for sc_i, state_constraint in enumerate(self.state_constraints_sym): if self.soft_constraints: opti.subject_to(state_constraint(x_var[:, i] + self.X_EQ.T) <= state_slack[sc_i]) - cost += soft_con_coeff*state_slack[sc_i]**2 + cost += soft_con_coeff * state_slack[sc_i]**2 opti.subject_to(state_slack[sc_i] >= 0) else: - opti.subject_to(state_constraint(x_var[:,i] + self.X_EQ.T) <= -self.constraint_tol) + opti.subject_to(state_constraint(x_var[:, i] + self.X_EQ.T) <= -self.constraint_tol) for ic_i, input_constraint in enumerate(self.input_constraints_sym): if self.soft_constraints: - opti.subject_to(input_constraint(u_var[:,i] + self.U_EQ.T) <= input_slack[ic_i]) - cost += soft_con_coeff*input_slack[ic_i]**2 + opti.subject_to(input_constraint(u_var[:, i] + self.U_EQ.T) <= input_slack[ic_i]) + cost += soft_con_coeff * input_slack[ic_i]**2 opti.subject_to(input_slack[ic_i] >= 0) else: - opti.subject_to(input_constraint(u_var[:,i] + self.U_EQ.T) <= -self.constraint_tol) + opti.subject_to(input_constraint(u_var[:, i] + self.U_EQ.T) <= -self.constraint_tol) # final state constraints for sc_i, state_constraint in enumerate(self.state_constraints_sym): @@ -194,54 +197,54 @@ def setup_optimizer(self): cost += soft_con_coeff * state_slack[sc_i] ** 2 opti.subject_to(state_slack[sc_i] >= 0) else: - opti.subject_to(state_constraint(x_var[:,-1] + self.X_EQ.T) <= -self.constraint_tol) + opti.subject_to(state_constraint(x_var[:, -1] + self.X_EQ.T) <= -self.constraint_tol) # initial condition constraints opti.subject_to(x_var[:, 0] == x_init) opti.minimize(cost) # create solver (IPOPT solver for now ) - opts = {"expand": True} - if platform == "linux": - opts.update({"print_time": 1, "print_header": 0}) - opti.solver('sqpmethod', opts) - elif platform == "darwin": - opts.update({"ipopt.max_iter": 100}) + opts = {'expand': True} + if platform == 'linux': + opts.update({'print_time': 1, 'print_header': 0}) + opti.solver(self.solver, opts) + elif platform == 'darwin': + opts.update({'ipopt.max_iter': 100}) opti.solver('ipopt', opts) else: - print("[ERROR]: CasADi solver tested on Linux and OSX only.") + print('[ERROR]: CasADi solver tested on Linux and OSX only.') exit() self.opti_dict = { - "opti": opti, - "x_var": x_var, - "u_var": u_var, - "x_init": x_init, - "x_ref": x_ref, - "cost": cost + 'opti': opti, + 'x_var': x_var, + 'u_var': u_var, + 'x_init': x_init, + 'x_ref': x_ref, + 'cost': cost } def select_action(self, - obs + obs, + info=None ): - """Solve nonlinear mpc problem to get next action. - + '''Solve nonlinear mpc problem to get next action. + Args: - obs (np.array): current state/observation. - + obs (ndarray): Current state/observation. + info (dict): Current info. + Returns: - action (np.array): input/action to the task/env. + action (ndarray): Input/action to the task/env. + ''' - """ - nx, nu = self.model.nx, self.model.nu - T = self.T opti_dict = self.opti_dict - opti = opti_dict["opti"] - x_var = opti_dict["x_var"] - u_var = opti_dict["u_var"] - x_init = opti_dict["x_init"] - x_ref = opti_dict["x_ref"] - cost = opti_dict["cost"] + opti = opti_dict['opti'] + x_var = opti_dict['x_var'] + u_var = opti_dict['u_var'] + x_init = opti_dict['x_init'] + x_ref = opti_dict['x_ref'] + # Assign the initial state. - opti.set_value(x_init, obs-self.X_EQ) + opti.set_value(x_init, obs - self.X_EQ) # Assign reference trajectory within horizon. goal_states = self.get_references() opti.set_value(x_ref, goal_states) @@ -266,7 +269,7 @@ def select_action(self, u_val = self.u_prev if u_val is None: print('[WARN]: MPC Infeasible first step.') - u_val = np.zeros((1,self.model.nu)) + u_val = np.zeros((1, self.model.nu)) elif return_status == 'Maximum_Iterations_Exceeded': self.terminate_loop = True u_val = opti.debug.value(u_var) diff --git a/safe_control_gym/controllers/mpc/linear_mpc.yaml b/safe_control_gym/controllers/mpc/linear_mpc.yaml index 59663e3d7..ba33d4708 100644 --- a/safe_control_gym/controllers/mpc/linear_mpc.yaml +++ b/safe_control_gym/controllers/mpc/linear_mpc.yaml @@ -5,7 +5,8 @@ r_mpc: q_mpc: - 1. use_prev_start: True - +prior_info: null +solver: sqpmethod # Runner args deque_size: 10 eval_batch_size: 10 diff --git a/safe_control_gym/controllers/mpc/mpc.py b/safe_control_gym/controllers/mpc/mpc.py index 16a364f42..6d7c1151b 100644 --- a/safe_control_gym/controllers/mpc/mpc.py +++ b/safe_control_gym/controllers/mpc/mpc.py @@ -1,39 +1,40 @@ -"""Model Predictive Control. - -""" -import numpy as np -import casadi as cs +'''Model Predictive Control.''' from copy import deepcopy +import casadi as cs +import numpy as np + from safe_control_gym.controllers.base_controller import BaseController -from safe_control_gym.controllers.mpc.mpc_utils import get_cost_weight_matrix, compute_discrete_lqr_gain_from_cont_linear_system, rk_discrete, compute_state_rmse +from safe_control_gym.controllers.mpc.mpc_utils import (compute_discrete_lqr_gain_from_cont_linear_system, + compute_state_rmse, get_cost_weight_matrix, + reset_constraints, rk_discrete) from safe_control_gym.envs.benchmark_env import Task -from safe_control_gym.envs.constraints import ConstraintList, GENERAL_CONSTRAINTS, create_constraint_list +from safe_control_gym.envs.constraints import GENERAL_CONSTRAINTS, create_constraint_list class MPC(BaseController): - """MPC with full nonlinear model. - - """ + '''MPC with full nonlinear model.''' def __init__( self, env_func, - horizon=5, - q_mpc=[1], - r_mpc=[1], - warmstart=True, - soft_constraints=False, - terminate_run_on_done=True, - constraint_tol: float=1e-6, + horizon: int = 5, + q_mpc: list = [1], + r_mpc: list = [1], + warmstart: bool = True, + soft_constraints: bool = False, + terminate_run_on_done: bool = True, + constraint_tol: float = 1e-6, # runner args # shared/base args - output_dir="results/temp", - additional_constraints=None, + output_dir: str = 'results/temp', + additional_constraints: list = None, + use_gpu: bool = False, + seed: int = 0, **kwargs - ): - """Creates task and controller. + ): + '''Creates task and controller. Args: env_func (Callable): function to instantiate task/environment. @@ -46,11 +47,16 @@ def __init__( constraint_tol (float): Tolerance to add the the constraint as sometimes solvers are not exact. output_dir (str): output directory to write logs and results. additional_constraints (list): List of additional constraints - - """ + use_gpu (bool): False (use cpu) True (use cuda). + seed (int): random seed. + ''' + super().__init__(env_func, output_dir, use_gpu, seed, **kwargs) for k, v in locals().items(): - if k != "self" and k != "kwargs" and "__" not in k: + if k != 'self' and k != 'kwargs' and '__' not in k: self.__dict__.update({k: v}) + + # if prior_info is None: + # self.prior_info = {} # Task. self.env = env_func() if additional_constraints is not None: @@ -58,84 +64,64 @@ def __init__( GENERAL_CONSTRAINTS, self.env) self.additional_constraints = additional_ConstraintsList.constraints - self.reset_constraints(self.env.constraints.constraints + self.additional_constraints) + self.constraints, self.state_constraints_sym, self.input_constraints_sym = reset_constraints(self.env.constraints.constraints + self.additional_constraints) else: - self.reset_constraints(self.env.constraints.constraints) + self.constraints, self.state_constraints_sym, self.input_constraints_sym = reset_constraints(self.env.constraints.constraints) self.additional_constraints = [] # Model parameters - self.model = self.env.symbolic + self.model = self.get_prior(self.env) self.dt = self.model.dt self.T = horizon self.Q = get_cost_weight_matrix(self.q_mpc, self.model.nx) self.R = get_cost_weight_matrix(self.r_mpc, self.model.nu) + # self.prior_info = prior_info self.soft_constraints = soft_constraints self.warmstart = warmstart self.terminate_run_on_done = terminate_run_on_done - #self.X_EQ = self.env.X_EQ - #self.U_EQ = self.env.U_EQ + # self.X_EQ = self.env.X_EQ + # self.U_EQ = self.env.U_EQ # logging - #self.logger = ExperimentLogger(output_dir) - - def reset_constraints(self, - constraints - ): - """ Setup the constraints list. - - Args: - constraints (list): List of constraints controller is subject to. - - """ - self.constraints = ConstraintList(constraints) - self.state_constraints_sym = self.constraints.get_state_constraint_symbolic_models() - self.input_constraints_sym = self.constraints.get_input_constraint_symbolic_models() - if len(self.constraints.input_state_constraints) > 0: - raise NotImplementedError('MPC cannot handle combined state input constraints yet.') + # self.logger = ExperimentLogger(output_dir) def add_constraints(self, constraints ): - """Add the constraints (from a list) to the system. + '''Add the constraints (from a list) to the system. Args: constraints (list): List of constraints controller is subject too. - - """ - self.reset_constraints(constraints + self.constraints.constraints) + ''' + self.constraints, self.state_constraints_sym, self.input_constraints_sym = reset_constraints(constraints + self.constraints.constraints) def remove_constraints(self, constraints ): - """Remove constraints from the current constraint list. + '''Remove constraints from the current constraint list. Args: constraints (list): list of constraints to be removed. - - """ + ''' old_constraints_list = self.constraints.constraints for constraint in constraints: - assert constraint in self.constraints.constraints,\ - ValueError("This constraint is not in the current list of constraints") + assert constraint in self.constraints.constraints, \ + ValueError('This constraint is not in the current list of constraints') old_constraints_list.remove(constraint) - self.reset_constraints(old_constraints_list) + self.constraints, self.state_constraints_sym, self.input_constraints_sym = reset_constraints(old_constraints_list) def close(self): - """Cleans up resources. - - """ + '''Cleans up resources.''' self.env.close() def reset(self): - """Prepares for training or evaluation. - - """ + '''Prepares for training or evaluation.''' # Setup reference input. if self.env.TASK == Task.STABILIZATION: - self.mode = "stabilization" + self.mode = 'stabilization' self.x_goal = self.env.X_GOAL elif self.env.TASK == Task.TRAJ_TRACKING: - self.mode = "tracking" + self.mode = 'tracking' self.traj = self.env.X_GOAL.T # Step along the reference. self.traj_step = 0 @@ -147,13 +133,11 @@ def reset(self): self.x_prev = None self.u_prev = None - self.reset_results_dict() + self.setup_results_dict() def set_dynamics_func(self): - """Updates symbolic dynamics with actual control frequency. - - """ - #self.dynamics_func = cs.integrator('fd', 'rk', + '''Updates symbolic dynamics with actual control frequency.''' + # self.dynamics_func = cs.integrator('fd', 'rk', # { # 'x': self.model.x_sym, # 'p': self.model.u_sym, @@ -166,29 +150,25 @@ def set_dynamics_func(self): self.dt) def compute_initial_guess(self, init_state, goal_states, x_lin, u_lin): - """Use LQR to get an initial guess of the """ + '''Use LQR to get an initial guess of the ''' dfdxdfdu = self.model.df_func(x=x_lin, u=u_lin) dfdx = dfdxdfdu['dfdx'].toarray() dfdu = dfdxdfdu['dfdu'].toarray() - lqr_gain, Ad, Bd = compute_discrete_lqr_gain_from_cont_linear_system(dfdx, dfdu, self.Q, self.R, self.dt) + lqr_gain, _, _ = compute_discrete_lqr_gain_from_cont_linear_system(dfdx, dfdu, self.Q, self.R, self.dt) - x_guess = np.zeros((self.model.nx, self.T+1)) + x_guess = np.zeros((self.model.nx, self.T + 1)) u_guess = np.zeros((self.model.nu, self.T)) x_guess[:, 0] = init_state for i in range(self.T): u = lqr_gain @ (x_guess[:, i] - goal_states[:, i]) + u_lin u_guess[:, i] = u - x_guess[:, i+1, None] = self.dynamics_func(x0=x_guess[:, i], p=u)['xf'].toarray() + x_guess[:, i + 1, None] = self.dynamics_func(x0=x_guess[:, i], p=u)['xf'].toarray() return x_guess, u_guess - - def setup_optimizer(self): - """Sets up nonlinear optimization problem. - - """ + '''Sets up nonlinear optimization problem.''' nx, nu = self.model.nx, self.model.nu T = self.T # Define optimizer and variables. @@ -215,14 +195,14 @@ def setup_optimizer(self): Xr=x_ref[:, i], Ur=np.zeros((nu, 1)), Q=self.Q, - R=self.R)["l"] + R=self.R)['l'] # Terminal cost. cost += cost_func(x=x_var[:, -1], u=np.zeros((nu, 1)), Xr=x_ref[:, -1], Ur=np.zeros((nu, 1)), Q=self.Q, - R=self.R)["l"] + R=self.R)['l'] # Constraints for i in range(self.T): # Dynamics constraints. @@ -232,17 +212,17 @@ def setup_optimizer(self): for sc_i, state_constraint in enumerate(self.state_constraints_sym): if self.soft_constraints: opti.subject_to(state_constraint(x_var[:, i]) <= state_slack[sc_i]) - cost += 10000*state_slack[sc_i]**2 + cost += 10000 * state_slack[sc_i]**2 opti.subject_to(state_slack[sc_i] >= 0) else: opti.subject_to(state_constraint(x_var[:, i]) < -self.constraint_tol) for ic_i, input_constraint in enumerate(self.input_constraints_sym): if self.soft_constraints: - opti.subject_to(input_constraint(u_var[:,i]) <= input_slack[ic_i]) - cost += 10000*input_slack[ic_i]**2 + opti.subject_to(input_constraint(u_var[:, i]) <= input_slack[ic_i]) + cost += 10000 * input_slack[ic_i]**2 opti.subject_to(input_slack[ic_i] >= 0) else: - opti.subject_to(input_constraint(u_var[:,i]) < -self.constraint_tol) + opti.subject_to(input_constraint(u_var[:, i]) < -self.constraint_tol) # Final state constraints. for sc_i, state_constraint in enumerate(self.state_constraints_sym): @@ -257,49 +237,51 @@ def setup_optimizer(self): opti.minimize(cost) # Create solver (IPOPT solver in this version) - #opts = {"ipopt.print_level": 0, "ipopt.sb": "yes", "print_time": 0} - opts = {"expand": True} + # opts = {'ipopt.print_level': 0, 'ipopt.sb': 'yes', 'print_time': 0} + opts = {'expand': True} opti.solver('ipopt', opts) self.opti_dict = { - "opti": opti, - "x_var": x_var, - "u_var": u_var, - "x_init": x_init, - "x_ref": x_ref, - "cost": cost + 'opti': opti, + 'x_var': x_var, + 'u_var': u_var, + 'x_init': x_init, + 'x_ref': x_ref, + 'cost': cost } def select_action(self, - obs + obs, + info=None ): - """Solves nonlinear mpc problem to get next action. + '''Solves nonlinear mpc problem to get next action. Args: - obs (np.array): current state/observation. - + obs (ndarray): Current state/observation. + info (dict): Current info + Returns: - np.array: input/action to the task/env. + action (ndarray): Input/action to the task/env. + ''' - """ opti_dict = self.opti_dict - opti = opti_dict["opti"] - x_var = opti_dict["x_var"] - u_var = opti_dict["u_var"] - x_init = opti_dict["x_init"] - x_ref = opti_dict["x_ref"] - cost = opti_dict["cost"] + opti = opti_dict['opti'] + x_var = opti_dict['x_var'] + u_var = opti_dict['u_var'] + x_init = opti_dict['x_init'] + x_ref = opti_dict['x_ref'] + # Assign the initial state. opti.set_value(x_init, obs) # Assign reference trajectory within horizon. goal_states = self.get_references() opti.set_value(x_ref, goal_states) - if self.mode == "tracking": + if self.mode == 'tracking': self.traj_step += 1 - #if self.warmstart and self.x_prev is None and self.u_prev is None: + # if self.warmstart and self.x_prev is None and self.u_prev is None: # x_guess, u_guess = self.compute_initial_guess(obs, goal_states, self.X_EQ, self.U_EQ) # opti.set_initial(x_var, x_guess) # opti.set_initial(u_var, u_guess) # Initial guess for optimization problem. - #elif self.warmstart and self.x_prev is not None and self.u_prev is not None: + # elif self.warmstart and self.x_prev is not None and self.u_prev is not None: if self.warmstart and self.x_prev is not None and self.u_prev is not None: # shift previous solutions by 1 step x_guess = deepcopy(self.x_prev) @@ -315,6 +297,7 @@ def select_action(self, self.u_prev = u_val self.results_dict['horizon_states'].append(deepcopy(self.x_prev)) self.results_dict['horizon_inputs'].append(deepcopy(self.u_prev)) + self.results_dict['goal_states'].append(deepcopy(goal_states)) self.results_dict['t_wall'].append(opti.stats()['t_wall_total']) # Take the first action from the solved action sequence. if u_val.ndim > 1: @@ -325,9 +308,7 @@ def select_action(self, return action def get_references(self): - """Constructs reference states along mpc horizon.(nx, T+1). - - """ + '''Constructs reference states along mpc horizon.(nx, T+1).''' if self.env.TASK == Task.STABILIZATION: # Repeat goal state for horizon steps. goal_states = np.tile(self.env.X_GOAL.reshape(-1, 1), (1, self.T + 1)) @@ -341,27 +322,26 @@ def get_references(self): np.tile(self.traj[:, -1:], (1, remain)) ], -1) else: - raise Exception("Reference for this mode is not implemented.") + raise Exception('Reference for this mode is not implemented.') return goal_states # (nx, T+1). - def reset_results_dict(self): - """ - - """ - self.results_dict = { 'obs': [], - 'reward': [], - 'done': [], - 'info': [], - 'action': [], - 'horizon_inputs': [], - 'horizon_states': [], - 'frames': [], - 'state_mse': [], - 'common_cost': [], - 'state': [], - 'state_error': [], - 't_wall': [] - } + def setup_results_dict(self): + '''Setup the results dictionary to store run information.''' + self.results_dict = {'obs': [], + 'reward': [], + 'done': [], + 'info': [], + 'action': [], + 'horizon_inputs': [], + 'horizon_states': [], + 'goal_states': [], + 'frames': [], + 'state_mse': [], + 'common_cost': [], + 'state': [], + 'state_error': [], + 't_wall': [] + } def run(self, env=None, @@ -370,16 +350,15 @@ def run(self, max_steps=None, terminate_run_on_done=None ): - """Runs evaluation with current policy. - + '''Runs evaluation with current policy. + Args: - render (bool): if to do real-time rendering. + render (bool): if to do real-time rendering. logging (bool): if to log on terminal. - - Returns: - dict: evaluation statisitcs, rendered frames. - """ + Returns: + dict: evaluation statisitcs, rendered frames. + ''' if env is None: env = self.env if terminate_run_on_done is None: @@ -389,19 +368,19 @@ def run(self, self.u_prev = None if not env.initial_reset: env.set_cost_function_param(self.Q, self.R) - #obs, info = env.reset() + # obs, info = env.reset() obs = env.reset() - print("Init State:") + print('Init State:') print(obs) ep_returns, ep_lengths = [], [] frames = [] - self.reset_results_dict() + self.setup_results_dict() self.results_dict['obs'].append(obs) self.results_dict['state'].append(env.state) i = 0 if env.TASK == Task.STABILIZATION: if max_steps is None: - MAX_STEPS = int(env.CTRL_FREQ*env.EPISODE_LEN_SEC) + MAX_STEPS = int(env.CTRL_FREQ * env.EPISODE_LEN_SEC) else: MAX_STEPS = max_steps elif env.TASK == Task.TRAJ_TRACKING: @@ -410,14 +389,14 @@ def run(self, else: MAX_STEPS = max_steps else: - raise("Undefined Task") + raise Exception('Undefined Task') self.terminate_loop = False done = False common_metric = 0 - while not(done and terminate_run_on_done) and i < MAX_STEPS and not (self.terminate_loop): + while not (done and terminate_run_on_done) and i < MAX_STEPS and not (self.terminate_loop): action = self.select_action(obs) if self.terminate_loop: - print("Infeasible MPC Problem") + print('Infeasible MPC Problem') break # Repeat input for more efficient control. obs, reward, done, info = env.step(action) @@ -427,10 +406,10 @@ def run(self, self.results_dict['info'].append(info) self.results_dict['action'].append(action) self.results_dict['state'].append(env.state) - self.results_dict['state_mse'].append(info["mse"]) - #self.results_dict['state_error'].append(env.state - env.X_GOAL[i,:]) + self.results_dict['state_mse'].append(info['mse']) + # self.results_dict['state_error'].append(env.state - env.X_GOAL[i,:]) - common_metric += info["mse"] + common_metric += info['mse'] print(i, '-th step.') print(action) print(obs) @@ -440,14 +419,14 @@ def run(self, print() if render: env.render() - frames.append(env.render("rgb_array")) + frames.append(env.render('rgb_array')) i += 1 # Collect evaluation results. ep_lengths = np.asarray(ep_lengths) ep_returns = np.asarray(ep_returns) if logging: - msg = "****** Evaluation ******\n" - msg += "eval_ep_length {:.2f} +/- {:.2f} | eval_ep_return {:.3f} +/- {:.3f}\n".format( + msg = '****** Evaluation ******\n' + msg += 'eval_ep_length {:.2f} +/- {:.2f} | eval_ep_return {:.3f} +/- {:.3f}\n'.format( ep_lengths.mean(), ep_lengths.std(), ep_returns.mean(), ep_returns.std()) if len(frames) != 0: @@ -461,6 +440,16 @@ def run(self, self.results_dict['total_rmse_state_error'] = compute_state_rmse(self.results_dict['state']) self.results_dict['total_rmse_obs_error'] = compute_state_rmse(self.results_dict['obs']) except ValueError: - raise Exception("[ERROR] mpc.run().py: MPC could not find a solution for the first step given the initial conditions. " - "Check to make sure initial conditions are feasible.") + raise Exception('[ERROR] mpc.run().py: MPC could not find a solution for the first step given the initial conditions. ' + 'Check to make sure initial conditions are feasible.') return deepcopy(self.results_dict) + + def reset_before_run(self, obs, info=None, env=None): + '''Reinitialize just the controller before a new run. + + Args: + obs (ndarray): The initial observation for the new run. + info (dict): The first info of the new run. + env (BenchmarkEnv): The environment to be used for the new run. + ''' + self.reset() diff --git a/safe_control_gym/controllers/mpc/mpc.yaml b/safe_control_gym/controllers/mpc/mpc.yaml index 59663e3d7..407961e6b 100644 --- a/safe_control_gym/controllers/mpc/mpc.yaml +++ b/safe_control_gym/controllers/mpc/mpc.yaml @@ -1,10 +1,12 @@ # MPC args -horizon: 10 +horizon: 50 r_mpc: - - 1. + - 0.1 q_mpc: - 1. -use_prev_start: True +warmstart: True +soft_constraints: False +constraint_tol: 1.0e-6 # Runner args deque_size: 10 diff --git a/safe_control_gym/controllers/mpc/mpc_utils.py b/safe_control_gym/controllers/mpc/mpc_utils.py index 396d387c0..090a0596f 100644 --- a/safe_control_gym/controllers/mpc/mpc_utils.py +++ b/safe_control_gym/controllers/mpc/mpc_utils.py @@ -1,62 +1,28 @@ -"""General MPC utility functions. +'''General MPC utility functions.''' -""" -import numpy as np import casadi as cs +import numpy as np import scipy +from safe_control_gym.controllers.lqr.lqr_utils import discretize_linear_system +from safe_control_gym.envs.constraints import ConstraintList + def get_cost_weight_matrix(weights, dim ): - """Gets weight matrix from input args. - - """ + '''Gets weight matrix from input args.''' if len(weights) == dim: W = np.diag(weights) elif len(weights) == 1: W = np.diag(weights * dim) else: - raise Exception("Wrong dimension for cost weights.") + raise Exception('Wrong dimension for cost weights.') return W -def discretize_linear_system(A, - B, - dt, - exact=False - ): - """Discretize a linear system. - - dx/dt = A x + B u - --> xd[k+1] = Ad xd[k] + Bd ud[k] where xd[k] = x(k*dt) - - Args: - A: np.array, system transition matrix. - B: np.array, input matrix. - dt: scalar, step time interval. - exact: bool, if to use exact discretization. - - Returns: - Discretized matrices Ad, Bd. - - """ - state_dim, input_dim = A.shape[1], B.shape[1] - if exact: - M = np.zeros((state_dim + input_dim, state_dim + input_dim)) - M[:state_dim, :state_dim] = A - M[:state_dim, state_dim:] = B - Md = scipy.linalg.expm(M * dt) - Ad = Md[:state_dim, :state_dim] - Bd = Md[:state_dim, state_dim:] - else: - I = np.eye(state_dim) - Ad = I + A * dt - Bd = B * dt - return Ad, Bd - def compute_discrete_lqr_gain_from_cont_linear_system(dfdx, dfdu, Q_lqr, R_lqr, dt): - """Computes the LQR gain used for propograting GP uncertainty from the prior model dynamics. + '''Computes the LQR gain used for propograting GP uncertainty from the prior model dynamics. Args: dfdx (np.array): CT A matrix @@ -66,8 +32,7 @@ def compute_discrete_lqr_gain_from_cont_linear_system(dfdx, dfdu, Q_lqr, R_lqr, Retrun: lqr_gain (np.array): LQR optimal gain, such that (A+BK) is hurwitz - - """ + ''' # Determine the LQR gain K to propogate the input uncertainty (doing this at each timestep will increase complexity). A, B = discretize_linear_system(dfdx, dfdu, dt) P = scipy.linalg.solve_discrete_are(A, B, Q_lqr, R_lqr) @@ -76,8 +41,9 @@ def compute_discrete_lqr_gain_from_cont_linear_system(dfdx, dfdu, Q_lqr, R_lqr, return lqr_gain, A, B + def rk_discrete(f, n, m, dt): - """Runge Kutta discretization for the function. + '''Runge Kutta discretization for the function. Args: f (casadi function): Function to discretize. @@ -87,23 +53,39 @@ def rk_discrete(f, n, m, dt): Return: x_next (casadi function?): - """ + ''' X = cs.SX.sym('X', n) U = cs.SX.sym('U', m) # Runge-Kutta 4 integration - k1 = f(X, U) - k2 = f(X+dt/2*k1, U) - k3 = f(X+dt/2*k2, U) - k4 = f(X+dt*k3, U) - x_next = X + dt/6*(k1+2*k2+2*k3+k4) + k1 = f(X, U) + k2 = f(X + dt / 2 * k1, U) + k3 = f(X + dt / 2 * k2, U) + k4 = f(X + dt * k3, U) + x_next = X + dt / 6 * (k1 + 2 * k2 + 2 * k3 + k4) rk_dyn = cs.Function('rk_f', [X, U], [x_next], ['x0', 'p'], ['xf']) return rk_dyn + def compute_state_rmse(state_error): - """Compute root-mean-square error.""" + '''Compute root-mean-square error.''' mse = np.mean(state_error ** 2, axis=0) state_rmse = np.sqrt(mse) state_rmse_scalar = np.sqrt(np.sum(mse)) return state_rmse, state_rmse_scalar + + +def reset_constraints(constraints): + '''Setup the constraints list. + + Args: + constraints (list): List of constraints controller is subject too. + ''' + + constraints_list = ConstraintList(constraints) + state_constraints_sym = constraints_list.get_state_constraint_symbolic_models() + input_constraints_sym = constraints_list.get_input_constraint_symbolic_models() + if len(constraints_list.input_state_constraints) > 0: + raise NotImplementedError('[Error] Cannot handle combined state input constraints yet.') + return constraints_list, state_constraints_sym, input_constraints_sym diff --git a/safe_control_gym/controllers/pid/pid.py b/safe_control_gym/controllers/pid/pid.py index 46a1a3e6a..6d070020a 100644 --- a/safe_control_gym/controllers/pid/pid.py +++ b/safe_control_gym/controllers/pid/pid.py @@ -1,158 +1,156 @@ -"""PID control class for Crazyflies. +'''PID control class for Crazyflies. Based on work conducted at UTIAS' DSL by SiQi Zhou and James Xu. +''' -""" -import numpy as np import math +import os + +import numpy as np import pybullet as p from scipy.spatial.transform import Rotation -from munch import munchify from safe_control_gym.controllers.base_controller import BaseController -from safe_control_gym.envs.benchmark_env import Task +from safe_control_gym.envs.benchmark_env import Environment, Task class PID(BaseController): - """ PID Class. - - """ + '''PID Class.''' def __init__(self, env_func=None, - g = 9.8, - KF = 3.16e-10, - KM = 7.94e-12, - P_COEFF_FOR = np.array([.4, .4, 1.25]), - I_COEFF_FOR = np.array([.05, .05, .05]), - D_COEFF_FOR = np.array([.2, .2, .5]), - P_COEFF_TOR = np.array([70000., 70000., 60000.]), - I_COEFF_TOR = np.array([.0, .0, 500.]), - D_COEFF_TOR = np.array([20000., 20000., 12000.]), - PWM2RPM_SCALE = 0.2685, - PWM2RPM_CONST = 4070.3, - MIN_PWM = 20000, - MAX_PWM = 65535, - MIXER_MATRIX = np.array([ [.5, -.5, -1], [.5, .5, 1], [-.5, .5, -1], [-.5, -.5, 1] ]), + g: float = 9.8, + kf: float = 3.16e-10, + km: float = 7.94e-12, + p_coeff_for=np.array([.4, .4, 1.25]), + i_coeff_for=np.array([.05, .05, .05]), + d_coeff_for=np.array([.2, .2, .5]), + p_coeff_tor=np.array([70000., 70000., 60000.]), + i_coeff_tor=np.array([.0, .0, 500.]), + d_coeff_tor=np.array([20000., 20000., 12000.]), + pwm2rpm_scale: float = 0.2685, + pwm2rpm_const: float = 4070.3, + min_pwm: float = 20000, + max_pwm: float = 65535, **kwargs ): - """Common control classes __init__ method. + '''Common control classes __init__ method. - Args + Args: g (float, optional): The gravitational acceleration in m/s^2. - - """ + kf (float, optional): Thrust coefficient. + km (float, optional): Torque coefficient. + p_coeff_for (ndarray, optional): Position proportional coefficients. + i_coeff_for (ndarray, optional): Position integral coefficients. + d_coeff_for (ndarray, optional): Position derivative coefficients. + p_coeff_tor (ndarray, optional): Attitude proportional coefficients. + i_coeff_tor (ndarray, optional): Attitude integral coefficients. + d_coeff_tor (ndarray, optional): Attitude derivative coefficients. + pwm2rpm_scale (float, optional): PWM-to-RPM scale factor. + pwm2rpm_const (float, optional): PWM-to-RPM constant factor. + min_pwm (float, optional): Minimum PWM. + max_pwm (float, optional): Maximum PWM. + ''' super().__init__(env_func, **kwargs) - self.GRAVITY = float(g) * 0.027 - self.KF = float(KF) - self.KM = float(KM) - self.P_COEFF_FOR = np.array(P_COEFF_FOR) - self.I_COEFF_FOR = np.array(I_COEFF_FOR) - self.D_COEFF_FOR = np.array(D_COEFF_FOR) - self.P_COEFF_TOR = np.array(P_COEFF_TOR) - self.I_COEFF_TOR = np.array(I_COEFF_TOR) - self.D_COEFF_TOR = np.array(D_COEFF_TOR) - self.PWM2RPM_SCALE = float(PWM2RPM_SCALE) - self.PWM2RPM_CONST = float(PWM2RPM_CONST) - self.MIN_PWM = float(MIN_PWM) - self.MAX_PWM = float(MAX_PWM) - self.MIXER_MATRIX = np.array(MIXER_MATRIX) + self.env = env_func() + + if self.env.NAME != Environment.QUADROTOR: + raise NotImplementedError('[ERROR] PID not implemented for any system other than Quadrotor (2D and 3D).') + + self.env.reset() + self.g = g + self.KF = kf + self.KM = km + self.P_COEFF_FOR = np.array(p_coeff_for) + self.I_COEFF_FOR = np.array(i_coeff_for) + self.D_COEFF_FOR = np.array(d_coeff_for) + self.P_COEFF_TOR = np.array(p_coeff_tor) + self.I_COEFF_TOR = np.array(i_coeff_tor) + self.D_COEFF_TOR = np.array(d_coeff_tor) + self.PWM2RPM_SCALE = np.array(pwm2rpm_scale) + self.PWM2RPM_CONST = np.array(pwm2rpm_const) + self.MIN_PWM = np.array(min_pwm) + self.MAX_PWM = np.array(max_pwm) + self.MIXER_MATRIX = np.array([[.5, -.5, -1], [.5, .5, 1], [-.5, .5, -1], [-.5, -.5, 1]]) - self.reset() + self.control_timestep = self.env.CTRL_TIMESTEP + self.reference = self.env.X_GOAL - def run(self, - iterations, - **kwargs - ): - """Computes the PID control action (as RPMs) for a single drone. + self.reset() - This methods sequentially calls `_dslPIDPositionControl()` and `_dslPIDAttitudeControl()`. - Parameter `cur_ang_vel` is unused. + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. Args: - control_timestep (float): The time step at which control is computed. - cur_pos (ndarray): (3,1)-shaped array of floats containing the current position. - cur_quat (ndarray): (4,1)-shaped array of floats containing the current orientation as a quaternion. - cur_vel (ndarray): (3,1)-shaped array of floats containing the current velocity. - cur_ang_vel (ndarray): (3,1)-shaped array of floats containing the current angular velocity. - target_pos (ndarray): (3,1)-shaped array of floats containing the desired position. - target_rpy (ndarray, optional): (3,1)-shaped array of floats containing the desired orientation as roll, pitch, yaw. - target_vel (ndarray, optional): (3,1)-shaped array of floats containing the desired velocity. - target_rpy_rates (ndarray, optional): (3,1)-shaped array of floats containing the desired roll, pitch, and yaw rates. + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. Returns: - ndarray: (4,1)-shaped array of integers containing the RPMs to apply to each of the 4 motors. - ndarray: (3,1)-shaped array of floats containing the current XYZ position error. - float: The current yaw error. - - """ - action = np.zeros(2) - - for i in range(iterations): - # Step the environment and print all returned information. - obs, reward, done, info = self.env.step(action) - - cur_pos=np.array([obs[0], 0, obs[2]]) - cur_quat=np.array(p.getQuaternionFromEuler([0, obs[4], 0])) - cur_vel=np.array([obs[1], 0, obs[3]]) - cur_ang_vel=np.array([0, obs[4], 0]) - + action (ndarray): The action chosen by the controller. + ''' + + step = self.extract_step(info) + + # Step the environment and print all returned information. + if self.env.QUAD_TYPE == 2: + cur_pos = np.array([obs[0], 0, obs[2]]) + cur_quat = np.array(p.getQuaternionFromEuler([0, obs[4], 0])) + cur_vel = np.array([obs[1], 0, obs[3]]) + elif self.env.QUAD_TYPE == 3: + cur_pos = np.array([obs[0], obs[2], obs[4]]) + cur_quat = np.array(p.getQuaternionFromEuler([obs[6], obs[7], obs[8]])) + cur_vel = np.array([obs[1], obs[3], obs[5]]) + + if self.env.QUAD_TYPE == 2: + if self.env.TASK == Task.TRAJ_TRACKING: + target_pos = np.array([self.reference[step, 0], + 0, + self.reference[step, 2]]) + target_vel = np.array([self.reference[step, 1], + 0, + self.reference[step, 3]]) + elif self.env.TASK == Task.STABILIZATION: + target_pos = np.array([self.reference[0], 0, self.reference[2]]) + target_vel = np.array([0, 0, 0]) + elif self.env.QUAD_TYPE == 3: if self.env.TASK == Task.TRAJ_TRACKING: - target_pos=np.array([ - self.reference[i-1,0], - 0, - self.reference[i-1,2] - ]) - target_vel=np.array([ - self.reference[i-1,1], - 0, - self.reference[i-1,3] - ]) + target_pos = np.array([self.reference[step, 0], + self.reference[step, 2], + self.reference[step, 4]]) + target_vel = np.array([self.reference[step, 1], + self.reference[step, 3], + self.reference[step, 5]]) elif self.env.TASK == Task.STABILIZATION: - target_pos=np.array([self.reference[0], 0, self.reference[2] ]) - target_vel=np.array([0, 0, 0 ]) - else: - raise NotImplementedError - - target_rpy = np.zeros(3) - target_rpy_rates = np.zeros(3) - - # Compute the next action. - self.control_counter += 1 - thrust, computed_target_rpy, pos_e = self._dslPIDPositionControl(self.control_timestep, - cur_pos, - cur_quat, - cur_vel, - target_pos, - target_rpy, - target_vel - ) - rpm = self._dslPIDAttitudeControl(self.control_timestep, - thrust, - cur_quat, - computed_target_rpy, - target_rpy_rates - ) - cur_rpy = p.getEulerFromQuaternion(cur_quat) - - action = rpm - action = self.KF * action**2 - action = np.array([action[0]+action[3], action[1]+action[2]]) - - self.results_dict['obs'].append(obs) - self.results_dict['reward'].append(reward) - self.results_dict['done'].append(done) - self.results_dict['info'].append(info) - self.results_dict['action'].append(action) - - self.close_results_dict() - - return self.results_dict - + target_pos = np.array([self.reference[0], self.reference[2], self.reference[4]]) + target_vel = np.array([0, 0, 0]) + + target_rpy = np.zeros(3) + target_rpy_rates = np.zeros(3) + + # Compute the next action. + thrust, computed_target_rpy, _ = self._dslPIDPositionControl(cur_pos, + cur_quat, + cur_vel, + target_pos, + target_rpy, + target_vel + ) + rpm = self._dslPIDAttitudeControl(thrust, + cur_quat, + computed_target_rpy, + target_rpy_rates + ) + + action = rpm + action = self.KF * action**2 + if self.env.QUAD_TYPE == 2: + action = np.array([action[0] + action[3], action[1] + action[2]]) + + return action + def _dslPIDPositionControl(self, - control_timestep, cur_pos, cur_quat, cur_vel, @@ -160,10 +158,9 @@ def _dslPIDPositionControl(self, target_rpy, target_vel ): - """DSL's CF2.x PID position control. + '''DSL's CF2.x PID position control. Args: - control_timestep (float): The time step at which control is computed. cur_pos (ndarray): (3,1)-shaped array of floats containing the current position. cur_quat (ndarray): (4,1)-shaped array of floats containing the current orientation as a quaternion. cur_vel (ndarray): (3,1)-shaped array of floats containing the current velocity. @@ -172,124 +169,120 @@ def _dslPIDPositionControl(self, target_vel (ndarray): (3,1)-shaped array of floats containing the desired velocity. Returns: - float: The target thrust along the drone z-axis. - ndarray: (3,1)-shaped array of floats containing the target roll, pitch, and yaw. - float: The current position error. + thrust (float): The target thrust along the drone z-axis. + target_euler (ndarray): (3,1)-shaped array of floats containing the target roll, pitch, and yaw. + pos_e (float): The current position error. + ''' - """ cur_rotation = np.array(p.getMatrixFromQuaternion(cur_quat)).reshape(3, 3) pos_e = target_pos - cur_pos vel_e = target_vel - cur_vel - self.integral_pos_e = self.integral_pos_e + pos_e*control_timestep + self.integral_pos_e = self.integral_pos_e + pos_e * self.control_timestep self.integral_pos_e = np.clip(self.integral_pos_e, -2., 2.) self.integral_pos_e[2] = np.clip(self.integral_pos_e[2], -0.15, .15) - + # PID target thrust. target_thrust = np.multiply(self.P_COEFF_FOR, pos_e) \ - + np.multiply(self.I_COEFF_FOR, self.integral_pos_e) \ - + np.multiply(self.D_COEFF_FOR, vel_e) + np.array([0, 0, self.GRAVITY]) - scalar_thrust = max(0., np.dot(target_thrust, cur_rotation[:,2])) - thrust = (math.sqrt(scalar_thrust / (4*self.KF)) - self.PWM2RPM_CONST) / self.PWM2RPM_SCALE + + np.multiply(self.I_COEFF_FOR, self.integral_pos_e) \ + + np.multiply(self.D_COEFF_FOR, vel_e) + np.array([0, 0, self.GRAVITY]) + scalar_thrust = max(0., np.dot(target_thrust, cur_rotation[:, 2])) + thrust = (math.sqrt(scalar_thrust / (4 * self.KF)) - self.PWM2RPM_CONST) / self.PWM2RPM_SCALE target_z_ax = target_thrust / np.linalg.norm(target_thrust) target_x_c = np.array([math.cos(target_rpy[2]), math.sin(target_rpy[2]), 0]) target_y_ax = np.cross(target_z_ax, target_x_c) / np.linalg.norm(np.cross(target_z_ax, target_x_c)) target_x_ax = np.cross(target_y_ax, target_z_ax) target_rotation = (np.vstack([target_x_ax, target_y_ax, target_z_ax])).transpose() - + # Target rotation. target_euler = (Rotation.from_matrix(target_rotation)).as_euler('XYZ', degrees=False) - + if np.any(np.abs(target_euler) > math.pi): - print("\n[ERROR] ctrl it", self.control_counter, "in Control._dslPIDPositionControl(), values outside range [-pi,pi]") - + raise ValueError('\n[ERROR] ctrl it', self.control_counter, 'in Control._dslPIDPositionControl(), values outside range [-pi,pi]') + return thrust, target_euler, pos_e - + def _dslPIDAttitudeControl(self, - control_timestep, thrust, cur_quat, target_euler, target_rpy_rates ): - """DSL's CF2.x PID attitude control. + '''DSL's CF2.x PID attitude control. Args: - control_timestep (float): The time step at which control is computed. thrust (float): The target thrust along the drone z-axis. cur_quat (ndarray): (4,1)-shaped array of floats containing the current orientation as a quaternion. target_euler (ndarray): (3,1)-shaped array of floats containing the computed target Euler angles. target_rpy_rates (ndarray): (3,1)-shaped array of floats containing the desired roll, pitch, and yaw rates. Returns: - ndarray: (4,1)-shaped array of integers containing the RPMs to apply to each of the 4 motors. + rpm (ndarray): (4,1)-shaped array of integers containing the RPMs to apply to each of the 4 motors. + ''' - """ cur_rotation = np.array(p.getMatrixFromQuaternion(cur_quat)).reshape(3, 3) cur_rpy = np.array(p.getEulerFromQuaternion(cur_quat)) target_quat = (Rotation.from_euler('XYZ', target_euler, degrees=False)).as_quat() - w,x,y,z = target_quat + w, x, y, z = target_quat target_rotation = (Rotation.from_quat([w, x, y, z])).as_matrix() - rot_matrix_e = np.dot((target_rotation.transpose()),cur_rotation) - np.dot(cur_rotation.transpose(),target_rotation) - rot_e = np.array([rot_matrix_e[2, 1], rot_matrix_e[0, 2], rot_matrix_e[1, 0]]) - rpy_rates_e = target_rpy_rates - (cur_rpy - self.last_rpy)/control_timestep + rot_matrix_e = np.dot((target_rotation.transpose()), cur_rotation) - np.dot(cur_rotation.transpose(), target_rotation) + rot_e = np.array([rot_matrix_e[2, 1], rot_matrix_e[0, 2], rot_matrix_e[1, 0]]) + rpy_rates_e = target_rpy_rates - (cur_rpy - self.last_rpy) / self.control_timestep self.last_rpy = cur_rpy - self.integral_rpy_e = self.integral_rpy_e - rot_e*control_timestep + self.integral_rpy_e = self.integral_rpy_e - rot_e * self.control_timestep self.integral_rpy_e = np.clip(self.integral_rpy_e, -1500., 1500.) self.integral_rpy_e[0:2] = np.clip(self.integral_rpy_e[0:2], -1., 1.) - + # PID target torques. target_torques = - np.multiply(self.P_COEFF_TOR, rot_e) \ - + np.multiply(self.D_COEFF_TOR, rpy_rates_e) \ - + np.multiply(self.I_COEFF_TOR, self.integral_rpy_e) + + np.multiply(self.D_COEFF_TOR, rpy_rates_e) \ + + np.multiply(self.I_COEFF_TOR, self.integral_rpy_e) target_torques = np.clip(target_torques, -3200, 3200) pwm = thrust + np.dot(self.MIXER_MATRIX, target_torques) pwm = np.clip(pwm, self.MIN_PWM, self.MAX_PWM) - - return self.PWM2RPM_SCALE * pwm + self.PWM2RPM_CONST - - def close(self): - """Cleans up resources. - """ - self.env.close() - - def close_results_dict(self): - """Cleanup the rtesults dict and munchify it. - - """ - self.results_dict['obs'] = np.vstack(self.results_dict['obs']) - self.results_dict['reward'] = np.vstack(self.results_dict['reward']) - self.results_dict['done'] = np.vstack(self.results_dict['done']) - self.results_dict['info'] = np.vstack(self.results_dict['info']) - self.results_dict['action'] = np.vstack(self.results_dict['action']) - - self.results_dict = munchify(self.results_dict) + return self.PWM2RPM_SCALE * pwm + self.PWM2RPM_CONST def reset(self): - """Resets the control classes. - - The previous step's and integral errors for both position and attitude are set to zero. + '''Resets the control classes. The previous step's and integral + errors for both position and attitude are set to zero. + ''' + self.model = self.get_prior(self.env, self.prior_info) + self.GRAVITY = self.g * self.model.quad_mass # The gravitational force (g*M) acting on each drone. + self.env.reset() + self.reset_before_run() - """ - self.env = self.env_func() - initial_obs, initial_info = self.env.reset() - self.control_timestep = self.env.CTRL_TIMESTEP - self.reference = initial_info['x_reference'] + def reset_before_run(self, obs=None, info=None, env=None): + '''Reinitialize just the controller before a new run. - self.control_counter = 0 - - # Clear the last roll, pitch, and yaw. - self.last_rpy = np.zeros(3) - + Args: + obs (ndarray): The initial observation for the new run. + info (dict): The first info of the new run. + env (BenchmarkEnv): The environment to be used for the new run. + ''' # Clear PID control variables. - self.last_pos_e = np.zeros(3) self.integral_pos_e = np.zeros(3) - self.last_rpy_e = np.zeros(3) + self.last_rpy = np.zeros(3) self.integral_rpy_e = np.zeros(3) + self.setup_results_dict() + + def close(self): + '''Shuts down and cleans up lingering resources.''' + self.env.close() + + def save(self, path): + '''Saves integral errors to checkpoint path. + + Args: + path (str): The path where to the saved integral errors. + ''' + path_dir = os.path.dirname(path) + os.makedirs(path_dir, exist_ok=True) + np.save(path, (self.integral_pos_e, self.last_rpy, self.integral_rpy_e)) + + def load(self, path): + '''Restores integral errors given checkpoint path. - self.results_dict = { 'obs': [], - 'reward': [], - 'done': [], - 'info': [], - 'action': [], - } + Args: + path (str): The path where the integral errors are saved. + ''' + self.integral_pos_e, self.last_rpy, self.integral_rpy_e = np.load(path) diff --git a/safe_control_gym/controllers/pid/pid.yaml b/safe_control_gym/controllers/pid/pid.yaml index 3bc3ccc2b..c0525471b 100644 --- a/safe_control_gym/controllers/pid/pid.yaml +++ b/safe_control_gym/controllers/pid/pid.yaml @@ -17,4 +17,4 @@ PWM2RPM_CONST: 4070.3 MIN_PWM: 20000 MAX_PWM: 65535 -# MIXER_MATRIX: [ [.5, -.5, -1], [.5, .5, 1], [-.5, .5, -1], [-.5, -.5, 1] ] \ No newline at end of file +# MIXER_MATRIX: [ [.5, -.5, -1], [.5, .5, 1], [-.5, .5, -1], [-.5, -.5, 1] ] diff --git a/safe_control_gym/controllers/ppo/ppo.py b/safe_control_gym/controllers/ppo/ppo.py index 89130de20..7111bd7c2 100644 --- a/safe_control_gym/controllers/ppo/ppo.py +++ b/safe_control_gym/controllers/ppo/ppo.py @@ -1,4 +1,4 @@ -"""Proximal Policy Optimization (PPO) +'''Proximal Policy Optimization (PPO) Based on: * https://github.com/openai/spinningup/blob/master/spinup/algos/pytorch/ppo/ppo.py @@ -10,34 +10,33 @@ * pytorch-a2c-ppo-acktr-gail - https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail * openai spinning up - ppo - https://github.com/openai/spinningup/tree/master/spinup/algos/pytorch/ppo * stable baselines3 - ppo - https://github.com/DLR-RM/stable-baselines3/tree/master/stable_baselines3/ppo - -""" +''' + import os import time + import numpy as np import torch -from collections import defaultdict - -from safe_control_gym.utils.logging import ExperimentLogger -from safe_control_gym.utils.utils import get_random_state, set_random_state, is_wrapped -from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer from safe_control_gym.controllers.base_controller import BaseController from safe_control_gym.controllers.ppo.ppo_utils import PPOAgent, PPOBuffer, compute_returns_and_advantages +from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics, + VecRecordEpisodeStatistics) +from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs +from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer, + RewardStdNormalizer) +from safe_control_gym.utils.logging import ExperimentLogger +from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state class PPO(BaseController): - """Proximal policy optimization. - - """ + '''Proximal policy optimization.''' def __init__(self, env_func, training=True, - checkpoint_path="model_latest.pt", - output_dir="temp", + checkpoint_path='model_latest.pt', + output_dir='temp', use_gpu=False, seed=0, **kwargs): @@ -64,7 +63,8 @@ def __init__(self, actor_lr=self.actor_lr, critic_lr=self.critic_lr, opt_epochs=self.opt_epochs, - mini_batch_size=self.mini_batch_size) + mini_batch_size=self.mini_batch_size, + activation=self.activation) self.agent.to(self.device) # Pre-/post-processing. self.obs_normalizer = BaseNormalizer() @@ -84,29 +84,25 @@ def __init__(self, self.logger = ExperimentLogger(output_dir, log_file_out=log_file_out, use_tensorboard=use_tensorboard) def reset(self): - """Do initializations for training or evaluation. - - """ + '''Do initializations for training or evaluation.''' if self.training: # set up stats tracking - self.env.add_tracker("constraint_violation", 0) - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("mse", 0, mode="queue") - + self.env.add_tracker('constraint_violation', 0) + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('mse', 0, mode='queue') + self.total_steps = 0 obs, _ = self.env.reset() self.obs = self.obs_normalizer(obs) else: # Add episodic stats to be tracked. - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.env.add_tracker("constraint_values", 0, mode="queue") - self.env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.env.add_tracker('constraint_values', 0, mode='queue') + self.env.add_tracker('mse', 0, mode='queue') def close(self): - """Shuts down and cleans up lingering resources. - - """ + '''Shuts down and cleans up lingering resources.''' self.env.close() if self.training: self.eval_env.close() @@ -115,22 +111,20 @@ def close(self): def save(self, path ): - """Saves model params and experiment state to checkpoint path. - - """ + '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) os.makedirs(path_dir, exist_ok=True) state_dict = { - "agent": self.agent.state_dict(), - "obs_normalizer": self.obs_normalizer.state_dict(), - "reward_normalizer": self.reward_normalizer.state_dict(), + 'agent': self.agent.state_dict(), + 'obs_normalizer': self.obs_normalizer.state_dict(), + 'reward_normalizer': self.reward_normalizer.state_dict(), } if self.training: exp_state = { - "total_steps": self.total_steps, - "obs": self.obs, - "random_state": get_random_state(), - "env_random_state": self.env.get_env_random_state() + 'total_steps': self.total_steps, + 'obs': self.obs, + 'random_state': get_random_state(), + 'env_random_state': self.env.get_env_random_state() } state_dict.update(exp_state) torch.save(state_dict, path) @@ -138,68 +132,78 @@ def save(self, def load(self, path ): - """Restores model and experiment given checkpoint path. - - """ + '''Restores model and experiment given checkpoint path.''' state = torch.load(path) # Restore policy. - self.agent.load_state_dict(state["agent"]) - self.obs_normalizer.load_state_dict(state["obs_normalizer"]) - self.reward_normalizer.load_state_dict(state["reward_normalizer"]) + self.agent.load_state_dict(state['agent']) + self.obs_normalizer.load_state_dict(state['obs_normalizer']) + self.reward_normalizer.load_state_dict(state['reward_normalizer']) # Restore experiment state. if self.training: - self.total_steps = state["total_steps"] - self.obs = state["obs"] - set_random_state(state["random_state"]) - self.env.set_env_random_state(state["env_random_state"]) + self.total_steps = state['total_steps'] + self.obs = state['obs'] + set_random_state(state['random_state']) + self.env.set_env_random_state(state['env_random_state']) self.logger.load(self.total_steps) def learn(self, env=None, **kwargs ): - """Performs learning (pre-training, training, fine-tuning, etc). - - """ + '''Performs learning (pre-training, training, fine-tuning, etc).''' while self.total_steps < self.max_env_steps: results = self.train_step() # Checkpoint. if self.total_steps >= self.max_env_steps or (self.save_interval and self.total_steps % self.save_interval == 0): # Latest/final checkpoint. self.save(self.checkpoint_path) - self.logger.info("Checkpoint | {}".format(self.checkpoint_path)) + self.logger.info(f'Checkpoint | {self.checkpoint_path}') if self.num_checkpoints and self.total_steps % (self.max_env_steps // self.num_checkpoints) == 0: # Intermediate checkpoint. - path = os.path.join(self.output_dir, "checkpoints", "model_{}.pt".format(self.total_steps)) + path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') self.save(path) # Evaluation. if self.eval_interval and self.total_steps % self.eval_interval == 0: eval_results = self.run(env=self.eval_env, n_episodes=self.eval_batch_size) - results["eval"] = eval_results - self.logger.info("Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}".format(eval_results["ep_lengths"].mean(), - eval_results["ep_lengths"].std(), - eval_results["ep_returns"].mean(), - eval_results["ep_returns"].std())) + results['eval'] = eval_results + self.logger.info('Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}'.format(eval_results['ep_lengths'].mean(), + eval_results['ep_lengths'].std(), + eval_results['ep_returns'].mean(), + eval_results['ep_returns'].std())) # Save best model. - eval_score = eval_results["ep_returns"].mean() - eval_best_score = getattr(self, "eval_best_score", -np.infty) + eval_score = eval_results['ep_returns'].mean() + eval_best_score = getattr(self, 'eval_best_score', -np.infty) if self.eval_save_best and eval_best_score < eval_score: self.eval_best_score = eval_score - self.save(os.path.join(self.output_dir, "model_best.pt")) + self.save(os.path.join(self.output_dir, 'model_best.pt')) # Logging. if self.log_interval and self.total_steps % self.log_interval == 0: self.log_step(results) + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. + + Args: + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. + + Returns: + action (ndarray): The action chosen by the controller. + ''' + + with torch.no_grad(): + obs = torch.FloatTensor(obs).to(self.device) + action = self.agent.ac.act(obs) + + return action + def run(self, env=None, render=False, n_episodes=10, verbose=False, - **kwargs ): - """Runs evaluation with current policy. - - """ + '''Runs evaluation with current policy.''' self.agent.eval() self.obs_normalizer.set_read_only() if env is None: @@ -208,36 +212,34 @@ def run(self, if not is_wrapped(env, RecordEpisodeStatistics): env = RecordEpisodeStatistics(env, n_episodes) # Add episodic stats to be tracked. - env.add_tracker("constraint_violation", 0, mode="queue") - env.add_tracker("constraint_values", 0, mode="queue") - env.add_tracker("mse", 0, mode="queue") - + env.add_tracker('constraint_violation', 0, mode='queue') + env.add_tracker('constraint_values', 0, mode='queue') + env.add_tracker('mse', 0, mode='queue') + obs, info = env.reset() obs = self.obs_normalizer(obs) ep_returns, ep_lengths = [], [] frames = [] while len(ep_returns) < n_episodes: - with torch.no_grad(): - obs = torch.FloatTensor(obs).to(self.device) - action = self.agent.ac.act(obs) - obs, reward, done, info = env.step(action) + action = self.select_action(obs=obs, info=info) + obs, _, done, info = env.step(action) if render: env.render() - frames.append(env.render("rgb_array")) + frames.append(env.render('rgb_array')) if verbose: - print("obs {} | act {}".format(obs, action)) + print(f'obs {obs} | act {action}') if done: - assert "episode" in info - ep_returns.append(info["episode"]["r"]) - ep_lengths.append(info["episode"]["l"]) + assert 'episode' in info + ep_returns.append(info['episode']['r']) + ep_lengths.append(info['episode']['l']) obs, _ = env.reset() obs = self.obs_normalizer(obs) # Collect evaluation results. ep_lengths = np.asarray(ep_lengths) ep_returns = np.asarray(ep_returns) - eval_results = {"ep_returns": ep_returns, "ep_lengths": ep_lengths} + eval_results = {'ep_returns': ep_returns, 'ep_lengths': ep_lengths} if len(frames) > 0: - eval_results["frames"] = frames + eval_results['frames'] = frames # Other episodic stats from evaluation env. if len(env.queued_stats) > 0: queued_stats = {k: np.asarray(v) for k, v in env.queued_stats.items()} @@ -245,15 +247,13 @@ def run(self, return eval_results def train_step(self): - """Performs a training/fine-tuning step. - - """ + '''Performs a training/fine-tuning step.''' self.agent.train() self.obs_normalizer.unset_read_only() rollouts = PPOBuffer(self.env.observation_space, self.env.action_space, self.rollout_steps, self.rollout_batch_size) obs = self.obs start = time.time() - for step in range(self.rollout_steps): + for _ in range(self.rollout_steps): with torch.no_grad(): act, v, logp = self.agent.ac.step(torch.FloatTensor(obs).to(self.device)) next_obs, rew, done, info = self.env.step(act) @@ -262,16 +262,16 @@ def train_step(self): mask = 1 - done.astype(float) # Time truncation is not the same as true termination. terminal_v = np.zeros_like(v) - for idx, inf in enumerate(info["n"]): - if "terminal_info" not in inf: + for idx, inf in enumerate(info['n']): + if 'terminal_info' not in inf: continue - inff = inf["terminal_info"] - if "TimeLimit.truncated" in inff and inff["TimeLimit.truncated"]: - terminal_obs = inf["terminal_observation"] + inff = inf['terminal_info'] + if 'TimeLimit.truncated' in inff and inff['TimeLimit.truncated']: + terminal_obs = inf['terminal_observation'] terminal_obs_tensor = torch.FloatTensor(terminal_obs).unsqueeze(0).to(self.device) terminal_val = self.agent.ac.critic(terminal_obs_tensor).squeeze().detach().cpu().numpy() terminal_v[idx] = terminal_val - rollouts.push({"obs": obs, "act": act, "rew": rew, "mask": mask, "v": v, "logp": logp, "terminal_v": terminal_v}) + rollouts.push({'obs': obs, 'act': act, 'rew': rew, 'mask': mask, 'v': v, 'logp': logp, 'terminal_v': terminal_v}) obs = next_obs self.obs = obs self.total_steps += self.rollout_batch_size * self.rollout_steps @@ -289,65 +289,63 @@ def train_step(self): # Prevent divide-by-0 for repetitive tasks. rollouts.adv = (adv - adv.mean()) / (adv.std() + 1e-6) results = self.agent.update(rollouts, self.device) - results.update({"step": self.total_steps, "elapsed_time": time.time() - start}) + results.update({'step': self.total_steps, 'elapsed_time': time.time() - start}) return results def log_step(self, results ): - """Does logging after a training step. - - """ - step = results["step"] + '''Does logging after a training step.''' + step = results['step'] # runner stats self.logger.add_scalars( { - "step": step, - "step_time": results["elapsed_time"], - "progress": step / self.max_env_steps + 'step': step, + 'step_time': results['elapsed_time'], + 'progress': step / self.max_env_steps }, step, - prefix="time", + prefix='time', write=False, write_tb=False) # Learning stats. self.logger.add_scalars( { - k: results[k] - for k in ["policy_loss", "value_loss", "entropy_loss", "approx_kl"] - }, - step, - prefix="loss") + k: results[k] + for k in ['policy_loss', 'value_loss', 'entropy_loss', 'approx_kl'] + }, + step, + prefix='loss') # Performance stats. ep_lengths = np.asarray(self.env.length_queue) ep_returns = np.asarray(self.env.return_queue) - ep_constraint_violation = np.asarray(self.env.queued_stats["constraint_violation"]) + ep_constraint_violation = np.asarray(self.env.queued_stats['constraint_violation']) self.logger.add_scalars( { - "ep_length": ep_lengths.mean(), - "ep_return": ep_returns.mean(), - "ep_reward": (ep_returns / ep_lengths).mean(), - "ep_constraint_violation": ep_constraint_violation.mean() + 'ep_length': ep_lengths.mean(), + 'ep_return': ep_returns.mean(), + 'ep_reward': (ep_returns / ep_lengths).mean(), + 'ep_constraint_violation': ep_constraint_violation.mean() }, step, - prefix="stat") + prefix='stat') # Total constraint violation during learning. - total_violations = self.env.accumulated_stats["constraint_violation"] - self.logger.add_scalars({"constraint_violation": total_violations}, step, prefix="stat") - if "eval" in results: - eval_ep_lengths = results["eval"]["ep_lengths"] - eval_ep_returns = results["eval"]["ep_returns"] - eval_constraint_violation = results["eval"]["constraint_violation"] - eval_mse = results["eval"]["mse"] + total_violations = self.env.accumulated_stats['constraint_violation'] + self.logger.add_scalars({'constraint_violation': total_violations}, step, prefix='stat') + if 'eval' in results: + eval_ep_lengths = results['eval']['ep_lengths'] + eval_ep_returns = results['eval']['ep_returns'] + eval_constraint_violation = results['eval']['constraint_violation'] + eval_mse = results['eval']['mse'] self.logger.add_scalars( { - "ep_length": eval_ep_lengths.mean(), - "ep_return": eval_ep_returns.mean(), - "ep_reward": (eval_ep_returns / eval_ep_lengths).mean(), - "constraint_violation": eval_constraint_violation.mean(), - "mse": eval_mse.mean() + 'ep_length': eval_ep_lengths.mean(), + 'ep_return': eval_ep_returns.mean(), + 'ep_reward': (eval_ep_returns / eval_ep_lengths).mean(), + 'constraint_violation': eval_constraint_violation.mean(), + 'mse': eval_mse.mean() }, step, - prefix="stat_eval") + prefix='stat_eval') # Print summary table self.logger.dump_scalars() diff --git a/safe_control_gym/controllers/ppo/ppo.yaml b/safe_control_gym/controllers/ppo/ppo.yaml index 6eee6f132..04126e91f 100644 --- a/safe_control_gym/controllers/ppo/ppo.yaml +++ b/safe_control_gym/controllers/ppo/ppo.yaml @@ -1,13 +1,14 @@ # Model args hidden_dim: 64 +activation: 'tanh' norm_obs: False norm_reward: False -clip_obs: 10. -clip_reward: 10. +clip_obs: 10 +clip_reward: 10 # Loss args -gamma: 0.99 -use_gae: False +gamma: 0.99 +use_gae: False gae_lambda: 0.95 use_clipped_value: False clip_param: 0.2 @@ -34,5 +35,5 @@ log_interval: 0 save_interval: 0 num_checkpoints: 0 eval_interval: 0 -eval_save_best: False +eval_save_best: False tensorboard: False diff --git a/safe_control_gym/controllers/ppo/ppo_utils.py b/safe_control_gym/controllers/ppo/ppo_utils.py index aa8aee54a..e08df5f76 100644 --- a/safe_control_gym/controllers/ppo/ppo_utils.py +++ b/safe_control_gym/controllers/ppo/ppo_utils.py @@ -1,22 +1,19 @@ -"""PPO utilities. +'''PPO utilities.''' + +from collections import defaultdict +from copy import deepcopy -""" import numpy as np import torch import torch.nn as nn +from gymnasium.spaces import Box -from gym.spaces import Box -from collections import defaultdict -from copy import deepcopy - -from safe_control_gym.math_and_models.neural_networks import MLP, CNN, RNN, init_ -from safe_control_gym.math_and_models.distributions import Normal, Categorical +from safe_control_gym.math_and_models.distributions import Categorical, Normal +from safe_control_gym.math_and_models.neural_networks import MLP class PPOAgent: - """A PPO class that encapsulates models, optimizers and update functions. - - """ + '''A PPO class that encapsulates models, optimizers and update functions.''' def __init__(self, obs_space, @@ -30,6 +27,7 @@ def __init__(self, critic_lr=0.001, opt_epochs=10, mini_batch_size=64, + activation='tanh', **kwargs ): # Parameters. @@ -41,11 +39,12 @@ def __init__(self, self.entropy_coef = entropy_coef self.opt_epochs = opt_epochs self.mini_batch_size = mini_batch_size + self.activation = activation # Model. self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, - activation="tanh") + activation=self.activation) # Optimizers. self.actor_opt = torch.optim.Adam(self.ac.actor.parameters(), actor_lr) self.critic_opt = torch.optim.Adam(self.ac.critic.parameters(), critic_lr) @@ -53,50 +52,38 @@ def __init__(self, def to(self, device ): - """Puts agent to device. - - """ + '''Puts agent to device.''' self.ac.to(device) def train(self): - """Sets training mode. - - """ + '''Sets training mode.''' self.ac.train() def eval(self): - """Sets evaluation mode. - - """ + '''Sets evaluation mode.''' self.ac.eval() def state_dict(self): - """Snapshots agent state. - - """ + '''Snapshots agent state.''' return { - "ac": self.ac.state_dict(), - "actor_opt": self.actor_opt.state_dict(), - "critic_opt": self.critic_opt.state_dict() + 'ac': self.ac.state_dict(), + 'actor_opt': self.actor_opt.state_dict(), + 'critic_opt': self.critic_opt.state_dict() } def load_state_dict(self, state_dict ): - """Restores agent state. - - """ - self.ac.load_state_dict(state_dict["ac"]) - self.actor_opt.load_state_dict(state_dict["actor_opt"]) - self.critic_opt.load_state_dict(state_dict["critic_opt"]) + '''Restores agent state.''' + self.ac.load_state_dict(state_dict['ac']) + self.actor_opt.load_state_dict(state_dict['actor_opt']) + self.critic_opt.load_state_dict(state_dict['critic_opt']) def compute_policy_loss(self, batch ): - """Returns policy loss(es) given batch of data. - - """ - obs, act, logp_old, adv = batch["obs"], batch["act"], batch["logp"], batch["adv"] + '''Returns policy loss(es) given batch of data.''' + obs, act, logp_old, adv = batch['obs'], batch['act'], batch['logp'], batch['adv'] dist, logp = self.ac.actor(obs, act) # Policy. ratio = torch.exp(logp - logp_old) @@ -111,10 +98,8 @@ def compute_policy_loss(self, def compute_value_loss(self, batch ): - """Returns value loss(es) given batch of data. - - """ - obs, ret, v_old = batch["obs"], batch["ret"], batch["v"] + '''Returns value loss(es) given batch of data.''' + obs, ret, v_old = batch['obs'], batch['ret'], batch['v'] v_cur = self.ac.critic(obs) if self.use_clipped_value: v_old_clipped = v_old + (v_cur - v_old).clamp(-self.clip_param, self.clip_param) @@ -127,20 +112,18 @@ def compute_value_loss(self, def update(self, rollouts, - device="cpu" + device='cpu' ): - """Updates model parameters based on current training batch. - - """ + '''Updates model parameters based on current training batch.''' results = defaultdict(list) num_mini_batch = rollouts.max_length * rollouts.batch_size // self.mini_batch_size - for i in range(self.opt_epochs): + for _ in range(self.opt_epochs): p_loss_epoch, v_loss_epoch, e_loss_epoch, kl_epoch = 0, 0, 0, 0 for batch in rollouts.sampler(self.mini_batch_size, device): # Actor update. policy_loss, entropy_loss, approx_kl = self.compute_policy_loss(batch) # Update only when no KL constraint or constraint is satisfied. - if (not self.target_kl > 0) or (self.target_kl > 0 and approx_kl <= 1.5 * self.target_kl): + if (self.target_kl <= 0) or (self.target_kl > 0 and approx_kl <= 1.5 * self.target_kl): self.actor_opt.zero_grad() (policy_loss + self.entropy_coef * entropy_loss).backward() self.actor_opt.step() @@ -153,18 +136,16 @@ def update(self, v_loss_epoch += value_loss.item() e_loss_epoch += entropy_loss.item() kl_epoch += approx_kl.item() - results["policy_loss"].append(p_loss_epoch / num_mini_batch) - results["value_loss"].append(v_loss_epoch / num_mini_batch) - results["entropy_loss"].append(e_loss_epoch / num_mini_batch) - results["approx_kl"].append(kl_epoch / num_mini_batch) + results['policy_loss'].append(p_loss_epoch / num_mini_batch) + results['value_loss'].append(v_loss_epoch / num_mini_batch) + results['entropy_loss'].append(e_loss_epoch / num_mini_batch) + results['approx_kl'].append(kl_epoch / num_mini_batch) results = {k: sum(v) / len(v) for k, v in results.items()} return results class MLPActor(nn.Module): - """Actor MLP model. - - """ + '''Actor MLP model.''' def __init__(self, obs_dim, @@ -173,9 +154,6 @@ def __init__(self, activation, discrete=False ): - """ - - """ super().__init__() self.pi_net = MLP(obs_dim, act_dim, hidden_dims, activation) # Construct output action distribution. @@ -190,9 +168,6 @@ def forward(self, obs, act=None ): - """ - - """ dist = self.dist_fn(self.pi_net(obs)) logp_a = None if act is not None: @@ -201,48 +176,36 @@ def forward(self, class MLPCritic(nn.Module): - """Critic MLP model. - - """ + '''Critic MLP model.''' def __init__(self, obs_dim, hidden_dims, activation ): - """ - - """ super().__init__() self.v_net = MLP(obs_dim, 1, hidden_dims, activation) def forward(self, obs ): - """ - - """ return self.v_net(obs) class MLPActorCritic(nn.Module): - """Model for the actor-critic agent. + '''Model for the actor-critic agent. Attributes: actor (MLPActor): policy network. critic (MLPCritic): value network. - - """ + ''' def __init__(self, obs_space, act_space, hidden_dims=(64, 64), - activation="tanh" + activation='tanh' ): - """ - - """ super().__init__() obs_dim = obs_space.shape[0] if isinstance(act_space, Box): @@ -259,9 +222,6 @@ def __init__(self, def step(self, obs ): - """ - - """ dist, _ = self.actor(obs) a = dist.sample() logp_a = dist.log_prob(a) @@ -271,24 +231,20 @@ def step(self, def act(self, obs ): - """ - - """ dist, _ = self.actor(obs) a = dist.mode() return a.cpu().numpy() class PPOBuffer(object): - """Storage for a batch of episodes during training. - + '''Storage for a batch of episodes during training. + Attributes: max_length (int): maximum length of episode. batch_size (int): number of episodes per batch. scheme (dict): describs shape & other info of data to be stored. keys (list): names of all data from scheme. - - """ + ''' def __init__(self, obs_space, @@ -306,73 +262,67 @@ def __init__(self, else: act_dim = act_space.n self.scheme = { - "obs": { - "vshape": (T, N, *obs_dim) + 'obs': { + 'vshape': (T, N, *obs_dim) }, - "act": { - "vshape": (T, N, act_dim) + 'act': { + 'vshape': (T, N, act_dim) }, - "rew": { - "vshape": (T, N, 1) + 'rew': { + 'vshape': (T, N, 1) }, - "mask": { - "vshape": (T, N, 1), - "init": np.ones + 'mask': { + 'vshape': (T, N, 1), + 'init': np.ones }, - "v": { - "vshape": (T, N, 1) + 'v': { + 'vshape': (T, N, 1) }, - "logp": { - "vshape": (T, N, 1) + 'logp': { + 'vshape': (T, N, 1) }, - "ret": { - "vshape": (T, N, 1) + 'ret': { + 'vshape': (T, N, 1) }, - "adv": { - "vshape": (T, N, 1) + 'adv': { + 'vshape': (T, N, 1) }, - "terminal_v": { - "vshape": (T, N, 1) + 'terminal_v': { + 'vshape': (T, N, 1) } } self.keys = list(self.scheme.keys()) self.reset() def reset(self): - """Allocates space for containers. - - """ + '''Allocates space for containers.''' for k, info in self.scheme.items(): - assert "vshape" in info, "Scheme must define vshape for {}".format(k) - vshape = info["vshape"] - dtype = info.get("dtype", np.float32) - init = info.get("init", np.zeros) + assert 'vshape' in info, f'Scheme must define vshape for {k}' + vshape = info['vshape'] + dtype = info.get('dtype', np.float32) + init = info.get('init', np.zeros) self.__dict__[k] = init(vshape, dtype=dtype) self.t = 0 def push(self, batch ): - """Inserts transition step data (as dict) to storage. - - """ + '''Inserts transition step data (as dict) to storage.''' for k, v in batch.items(): assert k in self.keys - shape = self.scheme[k]["vshape"][1:] - dtype = self.scheme[k].get("dtype", np.float32) + shape = self.scheme[k]['vshape'][1:] + dtype = self.scheme[k].get('dtype', np.float32) v_ = np.asarray(deepcopy(v), dtype=dtype).reshape(shape) self.__dict__[k][self.t] = v_ self.t = (self.t + 1) % self.max_length def get(self, - device="cpu" + device='cpu' ): - """Returns all data. - - """ + '''Returns all data.''' batch = {} for k, info in self.scheme.items(): - shape = info["vshape"][2:] + shape = info['vshape'][2:] data = self.__dict__[k].reshape(-1, *shape) batch[k] = torch.as_tensor(data, device=device) return batch @@ -380,23 +330,19 @@ def get(self, def sample(self, indices ): - """Returns partial data. - - """ + '''Returns partial data.''' batch = {} for k, info in self.scheme.items(): - shape = info["vshape"][2:] + shape = info['vshape'][2:] batch[k] = self.__dict__[k].reshape(-1, *shape)[indices] return batch def sampler(self, mini_batch_size, - device="cpu", + device='cpu', drop_last=True ): - """Makes sampler to loop through all data. - - """ + '''Makes sampler to loop through all data.''' total_steps = self.max_length * self.batch_size sampler = random_sample(np.arange(total_steps), mini_batch_size, drop_last) for indices in sampler: @@ -411,9 +357,7 @@ def random_sample(indices, batch_size, drop_last=True ): - """Returns index batches to iterate over. - - """ + '''Returns index batches to iterate over.''' indices = np.asarray(np.random.permutation(indices)) batches = indices[:len(indices) // batch_size * batch_size].reshape( -1, batch_size) @@ -434,9 +378,7 @@ def compute_returns_and_advantages(rews, use_gae=False, gae_lambda=0.95 ): - """Useful for policy-gradient algorithms. - - """ + '''Useful for policy-gradient algorithms.''' T, N = rews.shape[:2] rets, advs = np.zeros((T, N, 1)), np.zeros((T, N, 1)) ret, adv = last_val, np.zeros((N, 1)) diff --git a/safe_control_gym/controllers/rarl/rap.py b/safe_control_gym/controllers/rarl/rap.py index 8a208f787..2c42b4477 100644 --- a/safe_control_gym/controllers/rarl/rap.py +++ b/safe_control_gym/controllers/rarl/rap.py @@ -1,4 +1,4 @@ -"""Robust Adversarial Reinforcement Learning using Adversarial Populations (RAP) +'''Robust Adversarial Reinforcement Learning using Adversarial Populations (RAP) References papers & code: * [Robust Adversarial Reinforcement Learning](https://arxiv.org/abs/1703.02702) @@ -6,45 +6,37 @@ * [robust-adversarial-rl](https://github.com/jerinphilip/robust-adversarial-rl) * [rllab-adv](https://github.com/lerrel/rllab-adv) * [Robust Reinforcement Learning via adversary pools](https://github.com/eugenevinitsky/robust_RL_multi_adversary) +''' -Example: - train on cartpole_adversary:: - - $ python tests/test_main.py --mode train_two_phase --exp_id rap_cartpole_adv \ - --algo rap --task cartpole_adversary --num_workers 2 --max_env_steps 2000000 \ - --tensorboard --use_gae --num_adversaries 2 - -Todo: - * - -""" import os import time -import numpy as np -import torch from collections import defaultdict -from safe_control_gym.utils.logging import ExperimentLogger -from safe_control_gym.utils.utils import get_random_state, set_random_state, is_wrapped -from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer +import numpy as np +import torch from safe_control_gym.controllers.base_controller import BaseController from safe_control_gym.controllers.ppo.ppo_utils import PPOAgent, PPOBuffer, compute_returns_and_advantages from safe_control_gym.controllers.rarl.rarl_utils import split_obs_by_adversary +from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics, + VecRecordEpisodeStatistics) +from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs +from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer, + RewardStdNormalizer) +from safe_control_gym.utils.logging import ExperimentLogger +from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state class RAP(BaseController): - """rarl via adersarial population with PPO.""" - - def __init__(self, - env_func, - training=True, - checkpoint_path="model_latest.pt", - output_dir="temp", - use_gpu=False, - seed=0, + '''rarl via adersarial population with PPO.''' + + def __init__(self, + env_func, + training=True, + checkpoint_path='model_latest.pt', + output_dir='temp', + use_gpu=False, + seed=0, **kwargs): super().__init__(env_func, training, checkpoint_path, output_dir, use_gpu, seed, **kwargs) # task @@ -73,10 +65,10 @@ def __init__(self, self.agent = PPOAgent(self.env.observation_space, self.env.action_space, **shared_agent_args) self.agent.to(self.device) - # fetch adversary specs from env + # fetch adversary specs from env if self.training: - self.adv_obs_space = self.env.get_attr("adversary_observation_space")[0] - self.adv_act_space = self.env.get_attr("adversary_action_space")[0] + self.adv_obs_space = self.env.get_attr('adversary_observation_space')[0] + self.adv_act_space = self.env.get_attr('adversary_action_space')[0] else: self.adv_obs_space = self.env.adversary_observation_space self.adv_act_space = self.env.adversary_action_space @@ -104,72 +96,72 @@ def __init__(self, self.logger = ExperimentLogger(output_dir, log_file_out=log_file_out, use_tensorboard=use_tensorboard) def reset(self): - """Do initializations for training or evaluation.""" + '''Do initializations for training or evaluation.''' if self.training: # Add episodic stats to be tracked. - self.env.add_tracker("constraint_violation", 0) - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("mse", 0, mode="queue") - + self.env.add_tracker('constraint_violation', 0) + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('mse', 0, mode='queue') + self.total_steps = 0 obs, _ = self.env.reset() self.obs = self.obs_normalizer(obs) else: # Add episodic stats to be tracked. - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.env.add_tracker("constraint_values", 0, mode="queue") - self.env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.env.add_tracker('constraint_values', 0, mode='queue') + self.env.add_tracker('mse', 0, mode='queue') def close(self): - """Shuts down and cleans up lingering resources.""" + '''Shuts down and cleans up lingering resources.''' self.env.close() if self.training: self.eval_env.close() self.logger.close() def save(self, path): - """Saves model params and experiment state to checkpoint path.""" + '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) os.makedirs(path_dir, exist_ok=True) state_dict = { - "agent": self.agent.state_dict(), - "adversary": [adv.state_dict() for adv in self.adversaries], - "obs_normalizer": self.obs_normalizer.state_dict(), - "reward_normalizer": self.reward_normalizer.state_dict(), + 'agent': self.agent.state_dict(), + 'adversary': [adv.state_dict() for adv in self.adversaries], + 'obs_normalizer': self.obs_normalizer.state_dict(), + 'reward_normalizer': self.reward_normalizer.state_dict(), } if self.training: exp_state = { - "total_steps": self.total_steps, - "obs": self.obs, - "random_state": get_random_state(), - "env_random_state": self.env.get_env_random_state() + 'total_steps': self.total_steps, + 'obs': self.obs, + 'random_state': get_random_state(), + 'env_random_state': self.env.get_env_random_state() } state_dict.update(exp_state) torch.save(state_dict, path) def load(self, path): - """Restores model and experiment given checkpoint path.""" + '''Restores model and experiment given checkpoint path.''' state = torch.load(path) # restore pllicy - self.agent.load_state_dict(state["agent"]) - for i, adv_state_dict in enumerate(state["adversary"]): + self.agent.load_state_dict(state['agent']) + for i, adv_state_dict in enumerate(state['adversary']): self.adversaries[i].load_state_dict(adv_state_dict) - self.obs_normalizer.load_state_dict(state["obs_normalizer"]) - self.reward_normalizer.load_state_dict(state["reward_normalizer"]) + self.obs_normalizer.load_state_dict(state['obs_normalizer']) + self.reward_normalizer.load_state_dict(state['reward_normalizer']) # restore experiment state if self.training: - self.total_steps = state["total_steps"] - self.obs = state["obs"] - set_random_state(state["random_state"]) - self.env.set_env_random_state(state["env_random_state"]) + self.total_steps = state['total_steps'] + self.obs = state['obs'] + set_random_state(state['random_state']) + self.env.set_env_random_state(state['env_random_state']) self.logger.load(self.total_steps) def learn(self, env=None, **kwargs): - """Performs learning (pre-training, training, fine-tuning, etc).""" + '''Performs learning (pre-training, training, fine-tuning, etc).''' while self.total_steps < self.max_env_steps: results = self.train_step() @@ -177,33 +169,33 @@ def learn(self, env=None, **kwargs): if self.total_steps >= self.max_env_steps or (self.save_interval and self.total_steps % self.save_interval == 0): # latest/final checkpoint self.save(self.checkpoint_path) - self.logger.info("Checkpoint | {}".format(self.checkpoint_path)) + self.logger.info(f'Checkpoint | {self.checkpoint_path}') if self.num_checkpoints and self.total_steps % (self.max_env_steps // self.num_checkpoints) == 0: # intermediate checkpoint - path = os.path.join(self.output_dir, "checkpoints", "model_{}.pt".format(self.total_steps)) + path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') self.save(path) # eval if self.eval_interval and self.total_steps % self.eval_interval == 0: eval_results = self.run(env=self.eval_env, n_episodes=self.eval_batch_size) - results["eval"] = eval_results - self.logger.info("Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}".format(eval_results["ep_lengths"].mean(), - eval_results["ep_lengths"].std(), - eval_results["ep_returns"].mean(), - eval_results["ep_returns"].std())) + results['eval'] = eval_results + self.logger.info('Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}'.format(eval_results['ep_lengths'].mean(), + eval_results['ep_lengths'].std(), + eval_results['ep_returns'].mean(), + eval_results['ep_returns'].std())) # save best model - eval_score = eval_results["ep_returns"].mean() - eval_best_score = getattr(self, "eval_best_score", -np.infty) + eval_score = eval_results['ep_returns'].mean() + eval_best_score = getattr(self, 'eval_best_score', -np.infty) if self.eval_save_best and eval_best_score < eval_score: self.eval_best_score = eval_score - self.save(os.path.join(self.output_dir, "model_best.pt")) + self.save(os.path.join(self.output_dir, 'model_best.pt')) # logging if self.log_interval and self.total_steps % self.log_interval == 0: self.log_step(results) def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=False, **kwargs): - """Runs evaluation with current policy.""" + '''Runs evaluation with current policy.''' self.agent.eval() for adv in self.adversaries: adv.eval() @@ -213,9 +205,9 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=Fals else: if not is_wrapped(env, RecordEpisodeStatistics): env = RecordEpisodeStatistics(env, n_episodes) - env.add_tracker("constraint_violation", 0, mode="queue") - env.add_tracker("constraint_values", 0, mode="queue") - env.add_tracker("mse", 0, mode="queue") + env.add_tracker('constraint_violation', 0, mode='queue') + env.add_tracker('constraint_values', 0, mode='queue') + env.add_tracker('mse', 0, mode='queue') obs, _ = env.reset() obs = self.obs_normalizer(obs) @@ -236,26 +228,26 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=Fals action_adv = np.zeros(self.adv_act_space.shape[0]) env.set_adversary_control(action_adv) - obs, reward, done, info = env.step(action) + obs, _, done, info = env.step(action) if render: env.render() - frames.append(env.render("rgb_array")) + frames.append(env.render('rgb_array')) if verbose: - print("obs {} | act {}".format(obs, action)) + print(f'obs {obs} | act {action}') if done: - assert "episode" in info - ep_returns.append(info["episode"]["r"]) - ep_lengths.append(info["episode"]["l"]) + assert 'episode' in info + ep_returns.append(info['episode']['r']) + ep_lengths.append(info['episode']['l']) obs, _ = env.reset() obs = self.obs_normalizer(obs) # collect evaluation results ep_lengths = np.asarray(ep_lengths) ep_returns = np.asarray(ep_returns) - eval_results = {"ep_returns": ep_returns, "ep_lengths": ep_lengths} + eval_results = {'ep_returns': ep_returns, 'ep_lengths': ep_lengths} if len(frames) > 0: - eval_results["frames"] = frames + eval_results['frames'] = frames # Other episodic stats from evaluation env. if len(env.queued_stats) > 0: queued_stats = {k: np.asarray(v) for k, v in env.queued_stats.items()} @@ -263,7 +255,7 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=Fals return eval_results def train_step(self): - """Performs a training/fine-tuning step.""" + '''Performs a training/fine-tuning step.''' self.agent.train() for adv in self.adversaries: adv.train() @@ -280,88 +272,87 @@ def train_step(self): for adv_idx, adv_rollouts in rollout_splits: adv_results = self.adversaries[adv_idx].update(adv_rollouts) - adv_results = {k + "_adv{}".format(adv_idx): v for k, v in adv_results.items()} + adv_results = {k + f'_adv{adv_idx}': v for k, v in adv_results.items()} results.update(adv_results) # miscellaneous - results.update({"step": self.total_steps, "elapsed_time": time.time() - start, "adv_indices": [adv_idx for adv_idx, _ in rollout_splits]}) + results.update({'step': self.total_steps, 'elapsed_time': time.time() - start, 'adv_indices': [adv_idx for adv_idx, _ in rollout_splits]}) return results def log_step(self, results): - """Does logging after a training step.""" - step = results["step"] + '''Does logging after a training step.''' + step = results['step'] # runner stats self.logger.add_scalars( { - "step": step, - "time": results["elapsed_time"], - "progress": step / self.max_env_steps + 'step': step, + 'time': results['elapsed_time'], + 'progress': step / self.max_env_steps }, step, - prefix="time", + prefix='time', write=False, write_tb=False) # learning stats self.logger.add_scalars( { - k: results[k] - for k in ["policy_loss", "value_loss", "entropy_loss"] - }, - step, - prefix="loss") - for adv_idx in results["adv_indices"]: + k: results[k] + for k in ['policy_loss', 'value_loss', 'entropy_loss'] + }, + step, + prefix='loss') + for adv_idx in results['adv_indices']: self.logger.add_scalars( { - k: results[k + "_adv{}".format(adv_idx)] - for k in ["policy_loss", "value_loss", "entropy_loss"] + k: results[k + f'_adv{adv_idx}'] + for k in ['policy_loss', 'value_loss', 'entropy_loss'] }, step, - prefix="loss_adv{}".format(adv_idx)) + prefix=f'loss_adv{adv_idx}') # performance stats ep_lengths = np.asarray(self.env.length_queue) ep_returns = np.asarray(self.env.return_queue) - ep_constraint_violation = np.asarray(self.env.queued_stats["constraint_violation"]) + ep_constraint_violation = np.asarray(self.env.queued_stats['constraint_violation']) self.logger.add_scalars( { - "ep_length": ep_lengths.mean(), - "ep_return": ep_returns.mean(), - "ep_reward": (ep_returns / ep_lengths).mean(), - "ep_constraint_violation": ep_constraint_violation.mean() + 'ep_length': ep_lengths.mean(), + 'ep_return': ep_returns.mean(), + 'ep_reward': (ep_returns / ep_lengths).mean(), + 'ep_constraint_violation': ep_constraint_violation.mean() }, step, - prefix="stat") + prefix='stat') # Total constraint violation during learning. - total_violations = self.env.accumulated_stats["constraint_violation"] - self.logger.add_scalars({"constraint_violation": total_violations}, step, prefix="stat") - if "eval" in results: - eval_ep_lengths = results["eval"]["ep_lengths"] - eval_ep_returns = results["eval"]["ep_returns"] - eval_constraint_violation = results["eval"]["constraint_violation"] - eval_mse = results["eval"]["mse"] + total_violations = self.env.accumulated_stats['constraint_violation'] + self.logger.add_scalars({'constraint_violation': total_violations}, step, prefix='stat') + if 'eval' in results: + eval_ep_lengths = results['eval']['ep_lengths'] + eval_ep_returns = results['eval']['ep_returns'] + eval_constraint_violation = results['eval']['constraint_violation'] + eval_mse = results['eval']['mse'] self.logger.add_scalars( { - "ep_length": eval_ep_lengths.mean(), - "ep_return": eval_ep_returns.mean(), - "ep_reward": (eval_ep_returns / eval_ep_lengths).mean(), - "constraint_violation": eval_constraint_violation.mean(), - "mse": eval_mse.mean() + 'ep_length': eval_ep_lengths.mean(), + 'ep_return': eval_ep_returns.mean(), + 'ep_reward': (eval_ep_returns / eval_ep_lengths).mean(), + 'constraint_violation': eval_constraint_violation.mean(), + 'mse': eval_mse.mean() }, step, - prefix="stat_eval") + prefix='stat_eval') # print summary table self.logger.dump_scalars() def collect_rollouts(self): - """Gets trajectories (full episodes) for both agent and adversaries.""" + '''Gets trajectories (full episodes) for both agent and adversaries.''' # agent & adversary must have same obs & act space rollouts = PPOBuffer(self.env.observation_space, self.env.action_space, self.rollout_steps, self.rollout_batch_size) rollouts_adv = PPOBuffer(self.adv_obs_space, self.adv_act_space, self.rollout_steps, self.rollout_batch_size) # sample adversaries - adv_indices = np.random.randint(self.num_adversaries, size=self.rollout_batch_size) - adv_indices.sort() + adv_indices = sorted(np.random.randint(self.num_adversaries, size=self.rollout_batch_size)) indices_groups, indices_splits = np.unique(adv_indices, return_index=True) # sample trajectories @@ -369,7 +360,7 @@ def collect_rollouts(self): obs, _ = self.env.reset() obs = self.obs_normalizer(obs) - for step in range(self.rollout_steps): + for _ in range(self.rollout_steps): # get actions with torch.no_grad(): act, v, logp = self.agent.ac.step(torch.FloatTensor(obs).to(self.device)) @@ -385,7 +376,7 @@ def collect_rollouts(self): # step env # self.env.set_adversary_control(act_adv) act_adv_list = [[act] for act in act_adv] - self.env.env_method("set_adversary_control", act_adv_list) + self.env.env_method('set_adversary_control', act_adv_list) next_obs, rew, done, info = self.env.step(act) next_obs = self.obs_normalizer(next_obs) @@ -395,13 +386,13 @@ def collect_rollouts(self): # time truncation is not true termination terminal_v = np.zeros_like(v) terminal_v_adv = np.zeros_like(v_adv) - for idx, inf in enumerate(info["n"]): - # if "TimeLimit.truncated" in inf and inf["TimeLimit.truncated"]: - if "terminal_info" not in inf: + for idx, inf in enumerate(info['n']): + # if 'TimeLimit.truncated' in inf and inf['TimeLimit.truncated']: + if 'terminal_info' not in inf: continue - inff = inf["terminal_info"] - if "TimeLimit.truncated" in inff and inff["TimeLimit.truncated"]: - terminal_obs = inf["terminal_observation"] + inff = inf['terminal_info'] + if 'TimeLimit.truncated' in inff and inff['TimeLimit.truncated']: + terminal_obs = inf['terminal_observation'] terminal_obs_tensor = torch.FloatTensor(terminal_obs).unsqueeze(0).to(self.device) # estimate value for terminated state @@ -414,14 +405,14 @@ def collect_rollouts(self): terminal_v_adv[idx] = terminal_val_adv # collect rollout data - rollouts.push({"obs": obs, "act": act, "rew": rew, "mask": mask, "v": v, "logp": logp, "terminal_v": terminal_v}) + rollouts.push({'obs': obs, 'act': act, 'rew': rew, 'mask': mask, 'v': v, 'logp': logp, 'terminal_v': terminal_v}) # no need to push `obs`, `mask` since they are the same rollouts_adv.push({ - "act": act_adv, - "rew": -rew, - "v": v_adv, - "logp": logp_adv, - "terminal_v": terminal_v_adv, + 'act': act_adv, + 'rew': -rew, + 'v': v_adv, + 'logp': logp_adv, + 'terminal_v': terminal_v_adv, }) obs = next_obs @@ -476,4 +467,4 @@ def collect_rollouts(self): setattr(rollout_split, k, getattr(rollouts_adv, k)[:, s_idx:e_idx]) rollout_splits.append([idx, rollout_split]) - return rollouts, rollout_splits \ No newline at end of file + return rollouts, rollout_splits diff --git a/safe_control_gym/controllers/rarl/rap.yaml b/safe_control_gym/controllers/rarl/rap.yaml index 1003b09d2..125f7964c 100644 --- a/safe_control_gym/controllers/rarl/rap.yaml +++ b/safe_control_gym/controllers/rarl/rap.yaml @@ -37,5 +37,5 @@ log_interval: 0 save_interval: 0 num_checkpoints: 0 eval_interval: 0 -eval_save_best: False -tensorboard: False \ No newline at end of file +eval_save_best: False +tensorboard: False diff --git a/safe_control_gym/controllers/rarl/rarl.py b/safe_control_gym/controllers/rarl/rarl.py index 0542f76b3..4806689da 100644 --- a/safe_control_gym/controllers/rarl/rarl.py +++ b/safe_control_gym/controllers/rarl/rarl.py @@ -1,4 +1,4 @@ -"""Robust Adversarial Reinforcement Learning (RARL) +'''Robust Adversarial Reinforcement Learning (RARL) References papers & code: * [Robust Adversarial Reinforcement Learning](https://arxiv.org/abs/1703.02702) @@ -6,45 +6,39 @@ * [robust-adversarial-rl](https://github.com/jerinphilip/robust-adversarial-rl) * [rllab-adv](https://github.com/lerrel/rllab-adv) * [Robust Reinforcement Learning via adversary pools](https://github.com/eugenevinitsky/robust_RL_multi_adversary) +''' -Example: - train on cartpole with disturbance:: - - $ python mains/main.py --algo rarl --task cartpole --overrides benchmark/configs/overrides/rarl_cartpole.yaml - -Todo: - * - -""" import os import time -import numpy as np -import torch from collections import defaultdict -from safe_control_gym.utils.logging import ExperimentLogger -from safe_control_gym.utils.utils import get_random_state, set_random_state, is_wrapped -from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer +import numpy as np +import torch from safe_control_gym.controllers.base_controller import BaseController from safe_control_gym.controllers.ppo.ppo_utils import PPOAgent, PPOBuffer, compute_returns_and_advantages +from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics, + VecRecordEpisodeStatistics) +from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs +from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer, + RewardStdNormalizer) +from safe_control_gym.utils.logging import ExperimentLogger +from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state class RARL(BaseController): - """robust adversarial reinforcement learning with PPO.""" - - def __init__(self, - env_func, - training=True, - checkpoint_path="model_latest.pt", - output_dir="temp", - use_gpu=False, - seed=0, + '''robust adversarial reinforcement learning with PPO.''' + + def __init__(self, + env_func, + training=True, + checkpoint_path='model_latest.pt', + output_dir='temp', + use_gpu=False, + seed=0, **kwargs): super().__init__(env_func, training, checkpoint_path, output_dir, use_gpu, seed, **kwargs) - self.use_gpu = use_gpu + # task if self.training: # training (+ evaluation) @@ -71,10 +65,10 @@ def __init__(self, self.agent = PPOAgent(self.env.observation_space, self.env.action_space, **shared_agent_args) self.agent.to(self.device) - # fetch adversary specs from env + # fetch adversary specs from env if self.training: - self.adv_obs_space = self.env.get_attr("adversary_observation_space")[0] - self.adv_act_space = self.env.get_attr("adversary_action_space")[0] + self.adv_obs_space = self.env.get_attr('adversary_observation_space')[0] + self.adv_act_space = self.env.get_attr('adversary_action_space')[0] else: self.adv_obs_space = self.env.adversary_observation_space self.adv_act_space = self.env.adversary_action_space @@ -101,71 +95,71 @@ def __init__(self, self.logger = ExperimentLogger(output_dir, log_file_out=log_file_out, use_tensorboard=use_tensorboard) def reset(self): - """Do initializations for training or evaluation.""" + '''Do initializations for training or evaluation.''' if self.training: # Add episodic stats to be tracked. - self.env.add_tracker("constraint_violation", 0) - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("mse", 0, mode="queue") - + self.env.add_tracker('constraint_violation', 0) + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('mse', 0, mode='queue') + self.total_steps = 0 obs, _ = self.env.reset() self.obs = self.obs_normalizer(obs) else: # Add episodic stats to be tracked. - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.env.add_tracker("constraint_values", 0, mode="queue") - self.env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.env.add_tracker('constraint_values', 0, mode='queue') + self.env.add_tracker('mse', 0, mode='queue') def close(self): - """Shuts down and cleans up lingering resources.""" + '''Shuts down and cleans up lingering resources.''' self.env.close() if self.training: self.eval_env.close() self.logger.close() def save(self, path): - """Saves model params and experiment state to checkpoint path.""" + '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) os.makedirs(path_dir, exist_ok=True) state_dict = { - "agent": self.agent.state_dict(), - "adversary": self.adversary.state_dict(), - "obs_normalizer": self.obs_normalizer.state_dict(), - "reward_normalizer": self.reward_normalizer.state_dict(), + 'agent': self.agent.state_dict(), + 'adversary': self.adversary.state_dict(), + 'obs_normalizer': self.obs_normalizer.state_dict(), + 'reward_normalizer': self.reward_normalizer.state_dict(), } if self.training: exp_state = { - "total_steps": self.total_steps, - "obs": self.obs, - "random_state": get_random_state(), - "env_random_state": self.env.get_env_random_state() + 'total_steps': self.total_steps, + 'obs': self.obs, + 'random_state': get_random_state(), + 'env_random_state': self.env.get_env_random_state() } state_dict.update(exp_state) torch.save(state_dict, path) def load(self, path): - """Restores model and experiment given checkpoint path.""" + '''Restores model and experiment given checkpoint path.''' state = torch.load(path) # restore pllicy - self.agent.load_state_dict(state["agent"]) - self.adversary.load_state_dict(state["adversary"]) - self.obs_normalizer.load_state_dict(state["obs_normalizer"]) - self.reward_normalizer.load_state_dict(state["reward_normalizer"]) + self.agent.load_state_dict(state['agent']) + self.adversary.load_state_dict(state['adversary']) + self.obs_normalizer.load_state_dict(state['obs_normalizer']) + self.reward_normalizer.load_state_dict(state['reward_normalizer']) # restore experiment state if self.training: - self.total_steps = state["total_steps"] - self.obs = state["obs"] - set_random_state(state["random_state"]) - self.env.set_env_random_state(state["env_random_state"]) + self.total_steps = state['total_steps'] + self.obs = state['obs'] + set_random_state(state['random_state']) + self.env.set_env_random_state(state['env_random_state']) self.logger.load(self.total_steps) def learn(self, env=None, **kwargs): - """Performs learning (pre-training, training, fine-tuning, etc).""" + '''Performs learning (pre-training, training, fine-tuning, etc).''' while self.total_steps < self.max_env_steps: results = self.train_step() @@ -173,33 +167,50 @@ def learn(self, env=None, **kwargs): if self.total_steps >= self.max_env_steps or (self.save_interval and self.total_steps % self.save_interval == 0): # latest/final checkpoint self.save(self.checkpoint_path) - self.logger.info("Checkpoint | {}".format(self.checkpoint_path)) + self.logger.info(f'Checkpoint | {self.checkpoint_path}') if self.num_checkpoints and self.total_steps % (self.max_env_steps // self.num_checkpoints) == 0: # intermediate checkpoint - path = os.path.join(self.output_dir, "checkpoints", "model_{}.pt".format(self.total_steps)) + path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') self.save(path) # eval if self.eval_interval and self.total_steps % self.eval_interval == 0: eval_results = self.run(env=self.eval_env, n_episodes=self.eval_batch_size) - results["eval"] = eval_results - self.logger.info("Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}".format(eval_results["ep_lengths"].mean(), - eval_results["ep_lengths"].std(), - eval_results["ep_returns"].mean(), - eval_results["ep_returns"].std())) + results['eval'] = eval_results + self.logger.info('Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}'.format(eval_results['ep_lengths'].mean(), + eval_results['ep_lengths'].std(), + eval_results['ep_returns'].mean(), + eval_results['ep_returns'].std())) # save best model - eval_score = eval_results["ep_returns"].mean() - eval_best_score = getattr(self, "eval_best_score", -np.infty) + eval_score = eval_results['ep_returns'].mean() + eval_best_score = getattr(self, 'eval_best_score', -np.infty) if self.eval_save_best and eval_best_score < eval_score: self.eval_best_score = eval_score - self.save(os.path.join(self.output_dir, "model_best.pt")) + self.save(os.path.join(self.output_dir, 'model_best.pt')) # logging if self.log_interval and self.total_steps % self.log_interval == 0: self.log_step(results) - def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=False, **kwargs): - """Runs evaluation with current policy.""" + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. + + Args: + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. + + Returns: + action (ndarray): The action chosen by the controller. + ''' + + with torch.no_grad(): + obs = torch.FloatTensor(obs).to(self.device) + action = self.agent.ac.act(obs) + + return action + + def run(self, env=None, render=False, n_episodes=10, max_steps=1000, verbose=False, use_adv=False, **kwargs): + '''Runs evaluation with current policy.''' self.agent.eval() self.adversary.eval() self.obs_normalizer.set_read_only() @@ -208,19 +219,17 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=Fals else: if not is_wrapped(env, RecordEpisodeStatistics): env = RecordEpisodeStatistics(env, n_episodes) - env.add_tracker("constraint_violation", 0, mode="queue") - env.add_tracker("constraint_values", 0, mode="queue") - env.add_tracker("mse", 0, mode="queue") + env.add_tracker('constraint_violation', 0, mode='queue') + env.add_tracker('constraint_values', 0, mode='queue') + env.add_tracker('mse', 0, mode='queue') - obs, _ = env.reset() + obs, info = env.reset() obs = self.obs_normalizer(obs) ep_returns, ep_lengths = [], [] frames = [] while len(ep_returns) < n_episodes: - with torch.no_grad(): - obs = torch.FloatTensor(obs).to(self.device) - action = self.agent.ac.act(obs) + action = self.select_action(obs=obs, info=info) # no disturbance during testing if use_adv: @@ -230,26 +239,26 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=Fals action_adv = np.zeros(self.adv_act_space.shape[0]) env.set_adversary_control(action_adv) - obs, reward, done, info = env.step(action) + obs, _, done, info = env.step(action) if render: env.render() - frames.append(env.render("rgb_array")) + frames.append(env.render('rgb_array')) if verbose: - print("obs {} | act {}".format(obs, action)) + print(f'obs {obs} | act {action}') if done: - assert "episode" in info - ep_returns.append(info["episode"]["r"]) - ep_lengths.append(info["episode"]["l"]) + assert 'episode' in info + ep_returns.append(info['episode']['r']) + ep_lengths.append(info['episode']['l']) obs, _ = env.reset() obs = self.obs_normalizer(obs) # collect evaluation results ep_lengths = np.asarray(ep_lengths) ep_returns = np.asarray(ep_returns) - eval_results = {"ep_returns": ep_returns, "ep_lengths": ep_lengths} + eval_results = {'ep_returns': ep_returns, 'ep_lengths': ep_lengths} if len(frames) > 0: - eval_results["frames"] = frames + eval_results['frames'] = frames # Other episodic stats from evaluation env. if len(env.queued_stats) > 0: queued_stats = {k: np.asarray(v) for k, v in env.queued_stats.items()} @@ -257,7 +266,7 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, use_adv=Fals return eval_results def train_step(self): - """Performs a training/fine-tuning step.""" + '''Performs a training/fine-tuning step.''' self.obs_normalizer.unset_read_only() start = time.time() results = {} @@ -269,76 +278,76 @@ def train_step(self): results.update(adversary_results) # miscellaneous - results.update({"step": self.total_steps, "elapsed_time": time.time() - start}) + results.update({'step': self.total_steps, 'elapsed_time': time.time() - start}) return results def log_step(self, results): - """Does logging after a training step.""" - step = results["step"] + '''Does logging after a training step.''' + step = results['step'] # runner stats self.logger.add_scalars( { - "step": step, - "time": results["elapsed_time"], - "progress": step / self.max_env_steps + 'step': step, + 'time': results['elapsed_time'], + 'progress': step / self.max_env_steps }, step, - prefix="time", + prefix='time', write=False, write_tb=False) # learning stats self.logger.add_scalars( { - k: results[k] - for k in ["policy_loss", "value_loss", "entropy_loss"] - }, - step, - prefix="loss") + k: results[k] + for k in ['policy_loss', 'value_loss', 'entropy_loss'] + }, + step, + prefix='loss') self.logger.add_scalars( { - k: results[k + "_adv"] - for k in ["policy_loss", "value_loss", "entropy_loss"] - }, - step, - prefix="loss_adv") + k: results[k + '_adv'] + for k in ['policy_loss', 'value_loss', 'entropy_loss'] + }, + step, + prefix='loss_adv') # performance stats ep_lengths = np.asarray(self.env.length_queue) ep_returns = np.asarray(self.env.return_queue) - ep_constraint_violation = np.asarray(self.env.queued_stats["constraint_violation"]) + ep_constraint_violation = np.asarray(self.env.queued_stats['constraint_violation']) self.logger.add_scalars( { - "ep_length": ep_lengths.mean(), - "ep_return": ep_returns.mean(), - "ep_reward": (ep_returns / ep_lengths).mean(), - "ep_constraint_violation": ep_constraint_violation.mean() + 'ep_length': ep_lengths.mean(), + 'ep_return': ep_returns.mean(), + 'ep_reward': (ep_returns / ep_lengths).mean(), + 'ep_constraint_violation': ep_constraint_violation.mean() }, step, - prefix="stat") + prefix='stat') # Total constraint violation during learning. - total_violations = self.env.accumulated_stats["constraint_violation"] - self.logger.add_scalars({"constraint_violation": total_violations}, step, prefix="stat") - if "eval" in results: - eval_ep_lengths = results["eval"]["ep_lengths"] - eval_ep_returns = results["eval"]["ep_returns"] - eval_constraint_violation = results["eval"]["constraint_violation"] - eval_mse = results["eval"]["mse"] + total_violations = self.env.accumulated_stats['constraint_violation'] + self.logger.add_scalars({'constraint_violation': total_violations}, step, prefix='stat') + if 'eval' in results: + eval_ep_lengths = results['eval']['ep_lengths'] + eval_ep_returns = results['eval']['ep_returns'] + eval_constraint_violation = results['eval']['constraint_violation'] + eval_mse = results['eval']['mse'] self.logger.add_scalars( { - "ep_length": eval_ep_lengths.mean(), - "ep_return": eval_ep_returns.mean(), - "ep_reward": (eval_ep_returns / eval_ep_lengths).mean(), - "constraint_violation": eval_constraint_violation.mean(), - "mse": eval_mse.mean() + 'ep_length': eval_ep_lengths.mean(), + 'ep_return': eval_ep_returns.mean(), + 'ep_reward': (eval_ep_returns / eval_ep_lengths).mean(), + 'constraint_violation': eval_constraint_violation.mean(), + 'mse': eval_mse.mean() }, step, - prefix="stat_eval") + prefix='stat_eval') # print summary table self.logger.dump_scalars() def collect_rollouts(self, adversary=False): - """Uses current agent and adversary to collect trajectories.""" + '''Uses current agent and adversary to collect trajectories.''' if adversary: rollouts = PPOBuffer(self.adv_obs_space, self.adv_act_space, self.rollout_steps, self.rollout_batch_size) else: @@ -346,7 +355,7 @@ def collect_rollouts(self, adversary=False): obs = self.obs # get rollouts/trajectories - for step in range(self.rollout_steps): + for _ in range(self.rollout_steps): with torch.no_grad(): # protagnist action act, v, logp = self.agent.ac.step(torch.FloatTensor(obs).to(self.device)) @@ -355,7 +364,7 @@ def collect_rollouts(self, adversary=False): # step env act_adv_list = [[act] for act in act_adv] - self.env.env_method("set_adversary_control", act_adv_list) + self.env.env_method('set_adversary_control', act_adv_list) next_obs, rew, done, info = self.env.step(act) next_obs = self.obs_normalizer(next_obs) @@ -364,13 +373,13 @@ def collect_rollouts(self, adversary=False): # time truncation is not true termination terminal_v = np.zeros_like(v) - for idx, inf in enumerate(info["n"]): - # if "TimeLimit.truncated" in inf and inf["TimeLimit.truncated"]: - if "terminal_info" not in inf: + for idx, inf in enumerate(info['n']): + # if 'TimeLimit.truncated' in inf and inf['TimeLimit.truncated']: + if 'terminal_info' not in inf: continue - inff = inf["terminal_info"] - if "TimeLimit.truncated" in inff and inff["TimeLimit.truncated"]: - terminal_obs = inf["terminal_observation"] + inff = inf['terminal_info'] + if 'TimeLimit.truncated' in inff and inff['TimeLimit.truncated']: + terminal_obs = inf['terminal_observation'] terminal_obs_tensor = torch.FloatTensor(terminal_obs).unsqueeze(0).to(self.device) # estimate value for terminated state if adversary: @@ -380,20 +389,20 @@ def collect_rollouts(self, adversary=False): terminal_v[idx] = terminal_val # collect rollout data - rollout_data = {"obs": obs, "mask": mask, "terminal_v": terminal_v} + rollout_data = {'obs': obs, 'mask': mask, 'terminal_v': terminal_v} if adversary: rollout_data.update({ - "act": act_adv, - "rew": -rew, - "v": v_adv, - "logp": logp_adv, + 'act': act_adv, + 'rew': -rew, + 'v': v_adv, + 'logp': logp_adv, }) else: rollout_data.update({ - "act": act, - "rew": rew, - "v": v, - "logp": logp, + 'act': act, + 'rew': rew, + 'v': v, + 'logp': logp, }) rollouts.push(rollout_data) obs = next_obs @@ -419,7 +428,7 @@ def collect_rollouts(self, adversary=False): return rollouts def update_agent(self): - """Updates the protagonist agent per outer iteration.""" + '''Updates the protagonist agent per outer iteration.''' results = defaultdict(list) self.agent.train() self.adversary.eval() @@ -437,7 +446,7 @@ def update_agent(self): return results def update_adversary(self): - """Updates the adversary agent per outer iteration.""" + '''Updates the adversary agent per outer iteration.''' results = defaultdict(list) self.agent.eval() self.adversary.train() @@ -450,5 +459,5 @@ def update_adversary(self): results[key].append(val) # average stats - results = {k + "_adv": sum(v) / len(v) for k, v in results.items()} + results = {k + '_adv': sum(v) / len(v) for k, v in results.items()} return results diff --git a/safe_control_gym/controllers/rarl/rarl.yaml b/safe_control_gym/controllers/rarl/rarl.yaml index 4d6c0bd3b..eaeb09c08 100644 --- a/safe_control_gym/controllers/rarl/rarl.yaml +++ b/safe_control_gym/controllers/rarl/rarl.yaml @@ -23,9 +23,9 @@ max_grad_norm: 0.5 agent_iterations: 10 adversary_iterations: 10 -pretrained: null -train_protagonist: True -train_adversary: True +pretrained: null +train_protagonist: True +train_adversary: True # runner args max_env_steps: 1000000 @@ -40,5 +40,5 @@ log_interval: 0 save_interval: 0 num_checkpoints: 0 eval_interval: 0 -eval_save_best: False -tensorboard: False \ No newline at end of file +eval_save_best: False +tensorboard: False diff --git a/safe_control_gym/controllers/rarl/rarl_utils.py b/safe_control_gym/controllers/rarl/rarl_utils.py index 66330bdb2..84f4e761b 100644 --- a/safe_control_gym/controllers/rarl/rarl_utils.py +++ b/safe_control_gym/controllers/rarl/rarl_utils.py @@ -1,31 +1,21 @@ -from gym.spaces import Box, Discrete -from collections import defaultdict -from copy import deepcopy +'''RARL Utils.''' + import numpy as np -import torch -import torch.nn as nn from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs -from safe_control_gym.math_and_models.neural_networks import MLP, CNN, RNN, init_ -from safe_control_gym.math_and_models.distributions import Normal, Categorical - -# ----------------------------------------------------------------------------------- -# Misc -# ----------------------------------------------------------------------------------- def split_obs_by_adversary(obs, indices_splits): - """Splits and groups the observations by adversaries. - + '''Splits and groups the observations by adversaries. + Args: obs (np.array | dict): batch of observations, (N,*obs_dim). - indices_splits (np.array): shape (n,), + indices_splits (np.array): shape (n,), first occurence indices of the obs batch for each adversary. - + Returns: list: groups of observations for adversaries. - - """ + ''' obs_list = _unflatten_obs(obs) start = indices_splits end = np.concatenate([indices_splits[1:], [len(obs_list)]]) diff --git a/safe_control_gym/controllers/sac/sac.py b/safe_control_gym/controllers/sac/sac.py index f4184c1cb..646ef5b03 100644 --- a/safe_control_gym/controllers/sac/sac.py +++ b/safe_control_gym/controllers/sac/sac.py @@ -1,4 +1,4 @@ -"""Soft Actor Critic (SAC) +'''Soft Actor Critic (SAC) Adapted from https://github.com/openai/spinningup/blob/master/spinup/algos/pytorch/sac/sac.py @@ -10,39 +10,37 @@ * [rlkit - sac](https://github.com/vitchyr/rlkit/tree/7daf34b0ef2277d545a0ee792399a2ae6c3fb6ad/rlkit/torch/sac) * [ray rllib - sac](https://github.com/ray-project/ray/tree/master/rllib/agents/sac) * [curl - curl_sac](https://github.com/MishaLaskin/curl/blob/master/curl_sac.py) +''' -Todo: - * - -""" import os import time -import copy -import numpy as np -import torch from collections import defaultdict -from safe_control_gym.utils.logging import ExperimentLogger -from safe_control_gym.utils.utils import get_random_state, set_random_state, is_wrapped -from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs -from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer +import numpy as np +import torch from safe_control_gym.controllers.base_controller import BaseController from safe_control_gym.controllers.sac.sac_utils import SACAgent, SACBuffer +from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics, + VecRecordEpisodeStatistics) +from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs +from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs +from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer, + RewardStdNormalizer) +from safe_control_gym.utils.logging import ExperimentLogger +from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state class SAC(BaseController): - """soft actor critic.""" - - def __init__(self, - env_func, - training=True, - checkpoint_path="model_latest.pt", - output_dir="temp", - use_gpu=False, - seed=0, + '''soft actor critic.''' + + def __init__(self, + env_func, + training=True, + checkpoint_path='model_latest.pt', + output_dir='temp', + use_gpu=False, + seed=0, **kwargs): super().__init__(env_func, training, checkpoint_path, output_dir, use_gpu, seed, **kwargs) @@ -69,7 +67,8 @@ def __init__(self, target_entropy=self.target_entropy, actor_lr=self.actor_lr, critic_lr=self.critic_lr, - entropy_lr=self.entropy_lr) + entropy_lr=self.entropy_lr, + activation=self.activation) self.agent.to(self.device) # pre-/post-processing @@ -92,13 +91,13 @@ def __init__(self, self.logger = ExperimentLogger(output_dir, log_file_out=log_file_out, use_tensorboard=use_tensorboard) def reset(self): - """Prepares for training or testing.""" + '''Prepares for training or testing.''' if self.training: # set up stats tracking - self.env.add_tracker("constraint_violation", 0) - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0) + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('mse', 0, mode='queue') self.total_steps = 0 obs, _ = self.env.reset() @@ -106,96 +105,113 @@ def reset(self): self.buffer = SACBuffer(self.env.observation_space, self.env.action_space, self.max_buffer_size, self.train_batch_size) else: # set up stats tracking - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.env.add_tracker("constraint_values", 0, mode="queue") - self.env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.env.add_tracker('constraint_values', 0, mode='queue') + self.env.add_tracker('mse', 0, mode='queue') def close(self): - """Shuts down and cleans up lingering resources.""" + '''Shuts down and cleans up lingering resources.''' self.env.close() if self.training: self.eval_env.close() self.logger.close() - def save(self, path, save_buffer=True): - """Saves model params and experiment state to checkpoint path.""" + def save(self, path, save_buffer=False): + '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) os.makedirs(path_dir, exist_ok=True) state_dict = { - "agent": self.agent.state_dict(), - "obs_normalizer": self.obs_normalizer.state_dict(), - "reward_normalizer": self.reward_normalizer.state_dict() + 'agent': self.agent.state_dict(), + 'obs_normalizer': self.obs_normalizer.state_dict(), + 'reward_normalizer': self.reward_normalizer.state_dict() } if self.training: exp_state = { - "total_steps": self.total_steps, - "obs": self.obs, - "random_state": get_random_state(), - "env_random_state": self.env.get_env_random_state() + 'total_steps': self.total_steps, + 'obs': self.obs, + 'random_state': get_random_state(), + 'env_random_state': self.env.get_env_random_state() } # latest checkpoint shoud enable save_buffer (for experiment restore), # but intermediate checkpoint shoud not, to save storage (buffer is large) if save_buffer: - exp_state["buffer"] = self.buffer.state_dict() + exp_state['buffer'] = self.buffer.state_dict() state_dict.update(exp_state) torch.save(state_dict, path) def load(self, path): - """Restores model and experiment given checkpoint path.""" + '''Restores model and experiment given checkpoint path.''' state = torch.load(path) # restore params - self.agent.load_state_dict(state["agent"]) - self.obs_normalizer.load_state_dict(state["obs_normalizer"]) - self.reward_normalizer.load_state_dict(state["reward_normalizer"]) + self.agent.load_state_dict(state['agent']) + self.obs_normalizer.load_state_dict(state['obs_normalizer']) + self.reward_normalizer.load_state_dict(state['reward_normalizer']) # restore experiment state if self.training: - self.total_steps = state["total_steps"] - self.obs = state["obs"] - set_random_state(state["random_state"]) - self.env.set_env_random_state(state["env_random_state"]) - if "buffer" in state: - self.buffer.load_state_dict(state["buffer"]) + self.total_steps = state['total_steps'] + self.obs = state['obs'] + set_random_state(state['random_state']) + self.env.set_env_random_state(state['env_random_state']) + if 'buffer' in state: + self.buffer.load_state_dict(state['buffer']) self.logger.load(self.total_steps) def learn(self, env=None, **kwargs): - """Performs learning (pre-training, training, fine-tuning, etc).""" + '''Performs learning (pre-training, training, fine-tuning, etc).''' while self.total_steps < self.max_env_steps: results = self.train_step() # checkpoint if self.total_steps >= self.max_env_steps or (self.save_interval and self.total_steps % self.save_interval == 0): # latest/final checkpoint - self.save(self.checkpoint_path) - self.logger.info("Checkpoint | {}".format(self.checkpoint_path)) + self.save(self.checkpoint_path, save_buffer=False) + self.logger.info(f'Checkpoint | {self.checkpoint_path}') if self.num_checkpoints and self.total_steps % (self.max_env_steps // self.num_checkpoints) == 0: # intermediate checkpoint - path = os.path.join(self.output_dir, "checkpoints", "model_{}.pt".format(self.total_steps)) - self.save(path, save_buffer=False) + path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') + self.save(path, save_buffer=True) # eval if self.eval_interval and self.total_steps % self.eval_interval == 0: eval_results = self.run(env=self.eval_env, n_episodes=self.eval_batch_size) - results["eval"] = eval_results - self.logger.info("Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}".format(eval_results["ep_lengths"].mean(), - eval_results["ep_lengths"].std(), - eval_results["ep_returns"].mean(), - eval_results["ep_returns"].std())) + results['eval'] = eval_results + self.logger.info('Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}'.format(eval_results['ep_lengths'].mean(), + eval_results['ep_lengths'].std(), + eval_results['ep_returns'].mean(), + eval_results['ep_returns'].std())) # save best model - eval_score = eval_results["ep_returns"].mean() - eval_best_score = getattr(self, "eval_best_score", -np.infty) + eval_score = eval_results['ep_returns'].mean() + eval_best_score = getattr(self, 'eval_best_score', -np.infty) if self.eval_save_best and eval_best_score < eval_score: self.eval_best_score = eval_score - self.save(os.path.join(self.output_dir, "model_best.pt")) + self.save(os.path.join(self.output_dir, 'model_best.pt'), save_buffer=False) # logging if self.log_interval and self.total_steps % self.log_interval == 0: self.log_step(results) + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. + + Args: + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. + + Returns: + action (ndarray): The action chosen by the controller. + ''' + + with torch.no_grad(): + obs = torch.FloatTensor(obs).to(self.device) + action = self.agent.ac.act(obs, deterministic=True) + + return action + def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): - """Runs evaluation with current policy.""" + '''Runs evaluation with current policy.''' self.agent.eval() self.obs_normalizer.set_read_only() if env is None: @@ -204,9 +220,9 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): if not is_wrapped(env, RecordEpisodeStatistics): env = RecordEpisodeStatistics(env, n_episodes) # Add episodic stats to be tracked. - env.add_tracker("constraint_violation", 0, mode="queue") - env.add_tracker("constraint_values", 0, mode="queue") - env.add_tracker("mse", 0, mode="queue") + env.add_tracker('constraint_violation', 0, mode='queue') + env.add_tracker('constraint_values', 0, mode='queue') + env.add_tracker('mse', 0, mode='queue') obs, info = env.reset() obs = self.obs_normalizer(obs) @@ -214,30 +230,28 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): frames = [] while len(ep_returns) < n_episodes: - with torch.no_grad(): - obs = torch.FloatTensor(obs).to(self.device) - action = self.agent.ac.act(obs, deterministic=True) + action = self.select_action(obs=obs, info=info) - obs, reward, done, info = env.step(action) + obs, _, done, info = env.step(action) if render: env.render() - frames.append(env.render("rgb_array")) + frames.append(env.render('rgb_array')) if verbose: - print("obs {} | act {}".format(obs, action)) + print(f'obs {obs} | act {action}') if done: - assert "episode" in info - ep_returns.append(info["episode"]["r"]) - ep_lengths.append(info["episode"]["l"]) + assert 'episode' in info + ep_returns.append(info['episode']['r']) + ep_lengths.append(info['episode']['l']) obs, info = env.reset() obs = self.obs_normalizer(obs) # collect evaluation results ep_lengths = np.asarray(ep_lengths) ep_returns = np.asarray(ep_returns) - eval_results = {"ep_returns": ep_returns, "ep_lengths": ep_lengths} + eval_results = {'ep_returns': ep_returns, 'ep_lengths': ep_lengths} if len(frames) > 0: - eval_results["frames"] = frames + eval_results['frames'] = frames # Other episodic stats from evaluation env. if len(env.queued_stats) > 0: queued_stats = {k: np.asarray(v) for k, v in env.queued_stats.items()} @@ -245,18 +259,18 @@ def run(self, env=None, render=False, n_episodes=10, verbose=False, **kwargs): return eval_results def train_step(self, **kwargs): - """Performs a training step.""" + '''Performs a training step.''' self.agent.train() self.obs_normalizer.unset_read_only() obs = self.obs start = time.time() if self.total_steps < self.warm_up_steps: - act = np.stack([self.env.action_space.sample() for _ in range(self.rollout_batch_size)]) + action = np.stack([self.env.action_space.sample() for _ in range(self.rollout_batch_size)]) else: with torch.no_grad(): - act = self.agent.ac.act(torch.FloatTensor(obs).to(self.device), deterministic=False) - next_obs, rew, done, info = self.env.step(act) + action = self.agent.ac.act(torch.FloatTensor(obs).to(self.device), deterministic=False) + next_obs, rew, done, info = self.env.step(action) next_obs = self.obs_normalizer(next_obs) rew = self.reward_normalizer(rew, done) @@ -264,13 +278,13 @@ def train_step(self, **kwargs): # time truncation is not true termination terminal_idx, terminal_obs = [], [] - for idx, inf in enumerate(info["n"]): - if "terminal_info" not in inf: + for idx, inf in enumerate(info['n']): + if 'terminal_info' not in inf: continue - inff = inf["terminal_info"] - if "TimeLimit.truncated" in inff and inff["TimeLimit.truncated"]: + inff = inf['terminal_info'] + if 'TimeLimit.truncated' in inff and inff['TimeLimit.truncated']: terminal_idx.append(idx) - terminal_obs.append(inf["terminal_observation"]) + terminal_obs.append(inf['terminal_observation']) if len(terminal_obs) > 0: terminal_obs = _unflatten_obs(self.obs_normalizer(_flatten_obs(terminal_obs))) @@ -283,13 +297,13 @@ def train_step(self, **kwargs): true_next_obs = _flatten_obs(true_next_obs) self.buffer.push({ - "obs": obs, - "act": act, - "rew": rew, - # "next_obs": next_obs, - # "mask": mask, - "next_obs": true_next_obs, - "mask": true_mask, + 'obs': obs, + 'act': action, + 'rew': rew, + # 'next_obs': next_obs, + # 'mask': mask, + 'next_obs': true_next_obs, + 'mask': true_mask, }) obs = next_obs @@ -309,67 +323,67 @@ def train_step(self, **kwargs): results[k].append(v) results = {k: sum(v) / len(v) for k, v in results.items()} - results.update({"step": self.total_steps, "elapsed_time": time.time() - start}) + results.update({'step': self.total_steps, 'elapsed_time': time.time() - start}) return results def log_step(self, results): - """Does logging after a training step.""" - step = results["step"] + '''Does logging after a training step.''' + step = results['step'] # runner stats self.logger.add_scalars( { - "step": step, - "time": results["elapsed_time"], - "progress": step / self.max_env_steps, + 'step': step, + 'time': results['elapsed_time'], + 'progress': step / self.max_env_steps, }, step, - prefix="time", + prefix='time', write=False, write_tb=False) # learning stats - if "policy_loss" in results: + if 'policy_loss' in results: self.logger.add_scalars( { - k: results[k] - for k in ["policy_loss", "critic_loss", "entropy_loss"] - }, - step, - prefix="loss") + k: results[k] + for k in ['policy_loss', 'critic_loss', 'entropy_loss'] + }, + step, + prefix='loss') # performance stats ep_lengths = np.asarray(self.env.length_queue) ep_returns = np.asarray(self.env.return_queue) - ep_constraint_violation = np.asarray(self.env.queued_stats["constraint_violation"]) + ep_constraint_violation = np.asarray(self.env.queued_stats['constraint_violation']) self.logger.add_scalars( { - "ep_length": ep_lengths.mean(), - "ep_return": ep_returns.mean(), - "ep_reward": (ep_returns / ep_lengths).mean(), - "ep_constraint_violation": ep_constraint_violation.mean() + 'ep_length': ep_lengths.mean(), + 'ep_return': ep_returns.mean(), + 'ep_reward': (ep_returns / ep_lengths).mean(), + 'ep_constraint_violation': ep_constraint_violation.mean() }, step, - prefix="stat") + prefix='stat') # total constraint violation during learning - total_violations = self.env.accumulated_stats["constraint_violation"] - self.logger.add_scalars({"constraint_violation": total_violations}, step, prefix="stat") - - if "eval" in results: - eval_ep_lengths = results["eval"]["ep_lengths"] - eval_ep_returns = results["eval"]["ep_returns"] - eval_constraint_violation = results["eval"]["constraint_violation"] - eval_mse = results["eval"]["mse"] + total_violations = self.env.accumulated_stats['constraint_violation'] + self.logger.add_scalars({'constraint_violation': total_violations}, step, prefix='stat') + + if 'eval' in results: + eval_ep_lengths = results['eval']['ep_lengths'] + eval_ep_returns = results['eval']['ep_returns'] + eval_constraint_violation = results['eval']['constraint_violation'] + eval_mse = results['eval']['mse'] self.logger.add_scalars( { - "ep_length": eval_ep_lengths.mean(), - "ep_return": eval_ep_returns.mean(), - "ep_reward": (eval_ep_returns / eval_ep_lengths).mean(), - "constraint_violation": eval_constraint_violation.mean(), - "mse": eval_mse.mean() + 'ep_length': eval_ep_lengths.mean(), + 'ep_return': eval_ep_returns.mean(), + 'ep_reward': (eval_ep_returns / eval_ep_lengths).mean(), + 'constraint_violation': eval_constraint_violation.mean(), + 'mse': eval_mse.mean() }, step, - prefix="stat_eval") + prefix='stat_eval') # print summary table self.logger.dump_scalars() diff --git a/safe_control_gym/controllers/sac/sac.yaml b/safe_control_gym/controllers/sac/sac.yaml index 1756445ab..6cecd6667 100644 --- a/safe_control_gym/controllers/sac/sac.yaml +++ b/safe_control_gym/controllers/sac/sac.yaml @@ -1,5 +1,6 @@ # model args hidden_dim: 256 +activation: 'relu' norm_obs: False norm_reward: False clip_obs: 10. @@ -33,5 +34,5 @@ log_interval: 0 save_interval: 0 num_checkpoints: 0 eval_interval: 0 -eval_save_best: False +eval_save_best: False tensorboard: False diff --git a/safe_control_gym/controllers/sac/sac_utils.py b/safe_control_gym/controllers/sac/sac_utils.py index 2a233edf4..46531904d 100644 --- a/safe_control_gym/controllers/sac/sac_utils.py +++ b/safe_control_gym/controllers/sac/sac_utils.py @@ -1,14 +1,16 @@ +'''SAC Utils.''' + from collections import defaultdict from copy import deepcopy + import numpy as np import torch import torch.nn as nn import torch.nn.functional as F -from gym.spaces import Box, Discrete +from gymnasium.spaces import Box -from safe_control_gym.math_and_models.distributions import Normal, Categorical -from safe_control_gym.math_and_models.neural_networks import MLP, CNN, RNN, init_ -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer +from safe_control_gym.math_and_models.distributions import Categorical, Normal +from safe_control_gym.math_and_models.neural_networks import MLP # ----------------------------------------------------------------------------------- # Agent @@ -16,7 +18,7 @@ class SACAgent: - """A SAC class that encapsulates model, optimizer and update functions.""" + '''A SAC class that encapsulates model, optimizer and update functions.''' def __init__(self, obs_space, @@ -30,6 +32,7 @@ def __init__(self, actor_lr=0.001, critic_lr=0.001, entropy_lr=0.001, + activation='relu', **kwargs): # params self.obs_space = obs_space @@ -39,8 +42,10 @@ def __init__(self, self.tau = tau self.use_entropy_tuning = use_entropy_tuning + self.activation = activation + # model - self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, activation="relu") + self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, activation=self.activation) self.log_alpha = torch.tensor(np.log(init_temperature)) if self.use_entropy_tuning: @@ -63,48 +68,48 @@ def __init__(self, @property def alpha(self): - """Entropy-tuning parameter/temperature""" + '''Entropy-tuning parameter/temperature''' return self.log_alpha.exp() def to(self, device): - """Puts agent to device.""" + '''Puts agent to device.''' self.ac.to(device) self.ac_targ.to(device) self.log_alpha = self.log_alpha.to(device) def train(self): - """Sets training mode.""" + '''Sets training mode.''' self.ac.train() self.log_alpha.requires_grad = True def eval(self): - """Sets evaluation mode.""" + '''Sets evaluation mode.''' self.ac.eval() self.log_alpha.requires_grad = False def state_dict(self): - """Snapshots agent state.""" + '''Snapshots agent state.''' return { - "ac": self.ac.state_dict(), - "log_alpha": self.log_alpha, - "ac_targ": self.ac_targ.state_dict(), - "actor_opt": self.actor_opt.state_dict(), - "critic_opt": self.critic_opt.state_dict(), - "alpha_opt": self.alpha_opt.state_dict() + 'ac': self.ac.state_dict(), + 'log_alpha': self.log_alpha, + 'ac_targ': self.ac_targ.state_dict(), + 'actor_opt': self.actor_opt.state_dict(), + 'critic_opt': self.critic_opt.state_dict(), + 'alpha_opt': self.alpha_opt.state_dict() } def load_state_dict(self, state_dict): - """Restores agent state.""" - self.ac.load_state_dict(state_dict["ac"]) - self.log_alpha = state_dict["log_alpha"] - self.ac_targ.load_state_dict(state_dict["ac_targ"]) - self.actor_opt.load_state_dict(state_dict["actor_opt"]) - self.critic_opt.load_state_dict(state_dict["critic_opt"]) - self.alpha_opt.load_state_dict(state_dict["alpha_opt"]) + '''Restores agent state.''' + self.ac.load_state_dict(state_dict['ac']) + self.log_alpha = state_dict['log_alpha'] + self.ac_targ.load_state_dict(state_dict['ac_targ']) + self.actor_opt.load_state_dict(state_dict['actor_opt']) + self.critic_opt.load_state_dict(state_dict['critic_opt']) + self.alpha_opt.load_state_dict(state_dict['alpha_opt']) def compute_policy_loss(self, batch): - """Returns policy loss(es) given batch of data.""" - obs = batch["obs"] + '''Returns policy loss(es) given batch of data.''' + obs = batch['obs'] act, logp = self.ac.actor(obs, deterministic=False, with_logprob=True) q1 = self.ac.q1(obs, act) q2 = self.ac.q2(obs, act) @@ -117,8 +122,8 @@ def compute_policy_loss(self, batch): return policy_loss, entropy_loss def compute_q_loss(self, batch): - """Returns q-value loss(es) given batch of data.""" - obs, act, rew, next_obs, mask = batch["obs"], batch["act"], batch["rew"], batch["next_obs"], batch["mask"] + '''Returns q-value loss(es) given batch of data.''' + obs, act, rew, next_obs, mask = batch['obs'], batch['act'], batch['rew'], batch['next_obs'], batch['mask'] q1 = self.ac.q1(obs, act) q2 = self.ac.q2(obs, act) @@ -136,8 +141,8 @@ def compute_q_loss(self, batch): return critic_loss def update(self, batch): - """Updates model parameters based on current training batch.""" - resutls = defaultdict(list) + '''Updates model parameters based on current training batch.''' + results = defaultdict(list) # actor update policy_loss, entropy_loss = self.compute_policy_loss(batch) @@ -159,10 +164,10 @@ def update(self, batch): # update target networks soft_update(self.ac, self.ac_targ, self.tau) - resutls["policy_loss"] = policy_loss.item() - resutls["critic_loss"] = critic_loss.item() - resutls["entropy_loss"] = entropy_loss.item() - return resutls + results['policy_loss'] = policy_loss.item() + results['critic_loss'] = critic_loss.item() + results['entropy_loss'] = entropy_loss.item() + return results # ----------------------------------------------------------------------------------- @@ -248,14 +253,14 @@ def forward(self, obs, act): class MLPActorCritic(nn.Module): - """Model for the actor-critic agent. + '''Model for the actor-critic agent. Attributes: actor (MLPActor|MLPActorDiscrete): policy network. q1, q2 (MLPQFunction): q-value networks. - """ + ''' - def __init__(self, obs_space, act_space, hidden_dims=(64, 64), activation="relu"): + def __init__(self, obs_space, act_space, hidden_dims=(64, 64), activation='relu'): super().__init__() obs_dim = obs_space.shape[0] @@ -273,8 +278,10 @@ def __init__(self, obs_space, act_space, hidden_dims=(64, 64), activation="relu" low, high = act_space.low, act_space.high low = torch.FloatTensor(low) high = torch.FloatTensor(high) - # Rescale action from [-1, 1] to [low, high] - unscale_fn = lambda x: low.to(x.device) + (0.5 * (x + 1.0) * (high.to(x.device) - low.to(x.device))) + + def unscale_fn(x): # Rescale action from [-1, 1] to [low, high] + return low.to(x.device) + (0.5 * (x + 1.0) * (high.to(x.device) - low.to(x.device))) + self.actor = MLPActor(obs_dim, act_dim, hidden_dims, activation, postprocess_fn=unscale_fn) # Q functions @@ -283,7 +290,7 @@ def __init__(self, obs_space, act_space, hidden_dims=(64, 64), activation="relu" def act(self, obs, deterministic=False): a, _ = self.actor(obs, deterministic, False) - return a.numpy() + return a.cpu().numpy() # ----------------------------------------------------------------------------------- @@ -292,14 +299,14 @@ def act(self, obs, deterministic=False): class SACBuffer(object): - """Storage for replay buffer during training. + '''Storage for replay buffer during training. Attributes: max_size (int): maximum size of the replay buffer. batch_size (int): number of samples (steps) per batch. scheme (dict): describs shape & other info of data to be stored. keys (list): names of all data from scheme. - """ + ''' def __init__(self, obs_space, act_space, max_size, batch_size=None): super().__init__() @@ -314,44 +321,44 @@ def __init__(self, obs_space, act_space, max_size, batch_size=None): N = max_size self.scheme = { - "obs": { - "vshape": (N, *obs_dim) + 'obs': { + 'vshape': (N, *obs_dim) }, - "next_obs": { - "vshape": (N, *obs_dim) + 'next_obs': { + 'vshape': (N, *obs_dim) }, - "act": { - "vshape": (N, act_dim) + 'act': { + 'vshape': (N, act_dim) }, - "rew": { - "vshape": (N, 1) + 'rew': { + 'vshape': (N, 1) }, - "mask": { - "vshape": (N, 1), - "init": np.ones + 'mask': { + 'vshape': (N, 1), + 'init': np.ones } } self.keys = list(self.scheme.keys()) self.reset() def reset(self): - """Allocate space for containers.""" + '''Allocate space for containers.''' for k, info in self.scheme.items(): - assert "vshape" in info, "Scheme must define vshape for {}".format(k) - vshape = info["vshape"] - dtype = info.get("dtype", np.float32) - init = info.get("init", np.zeros) + assert 'vshape' in info, f'Scheme must define vshape for {k}' + vshape = info['vshape'] + dtype = info.get('dtype', np.float32) + init = info.get('init', np.zeros) self.__dict__[k] = init(vshape, dtype=dtype) self.pos = 0 self.buffer_size = 0 def __len__(self): - """Returns current size of the buffer.""" + '''Returns current size of the buffer.''' return self.buffer_size def state_dict(self): - """Returns a snapshot of current buffer.""" + '''Returns a snapshot of current buffer.''' state = dict( pos=self.pos, buffer_size=self.buffer_size, @@ -362,19 +369,19 @@ def state_dict(self): return state def load_state_dict(self, state): - """Restores buffer from previous state.""" + '''Restores buffer from previous state.''' for k, v in state.items(): self.__dict__[k] = v def push(self, batch): - """Inserts transition step data (as dict) to storage.""" + '''Inserts transition step data (as dict) to storage.''' # batch size k = list(batch.keys())[0] n = batch[k].shape[0] for k, v in batch.items(): - shape = self.scheme[k]["vshape"][1:] - dtype = self.scheme[k].get("dtype", np.float32) + shape = self.scheme[k]['vshape'][1:] + dtype = self.scheme[k].get('dtype', np.float32) v_ = np.asarray(v, dtype=dtype).reshape((n,) + shape) if self.pos + n <= self.max_size: @@ -390,14 +397,14 @@ def push(self, batch): self.pos = (self.pos + n) % self.max_size def sample(self, batch_size=None, device=None): - """Returns data batch.""" + '''Returns data batch.''' if not batch_size: batch_size = self.batch_size indices = np.random.randint(0, len(self), size=batch_size) batch = {} for k, info in self.scheme.items(): - shape = info["vshape"][1:] + shape = info['vshape'][1:] v = self.__dict__[k].reshape(-1, *shape)[indices] if device is None: batch[k] = torch.as_tensor(v) @@ -412,12 +419,12 @@ def sample(self, batch_size=None, device=None): def soft_update(source, target, tau): - """Synchronizes target networks with exponential moving average.""" + '''Synchronizes target networks with exponential moving average.''' for target_param, param in zip(target.parameters(), source.parameters()): target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau) def hard_update(source, target): - """Synchronizes target networks by copying over parameters directly.""" + '''Synchronizes target networks by copying over parameters directly.''' for target_param, param in zip(target.parameters(), source.parameters()): - target_param.data.copy_(param.data) \ No newline at end of file + target_param.data.copy_(param.data) diff --git a/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py b/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py index c5174c3e7..533d10b61 100644 --- a/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py +++ b/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py @@ -1,26 +1,19 @@ -"""Utility function for a generic safe explorer. +'''Utility function for a generic safe explorer.''' + +import warnings -""" import numpy as np import torch import torch.nn as nn import torch.nn.functional as F +from gymnasium.spaces import Box -from gym.spaces import Box -from collections import defaultdict -from copy import deepcopy -import warnings - -from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs -from safe_control_gym.math_and_models.neural_networks import MLP, CNN, RNN, init_ -from safe_control_gym.math_and_models.distributions import Normal, Categorical from safe_control_gym.controllers.ppo.ppo_utils import random_sample +from safe_control_gym.math_and_models.neural_networks import MLP class SafetyLayer: - """Layer to learn constraint models and to impose action projection. - - """ + '''Layer to learn constraint models and to impose action projection.''' def __init__(self, obs_space, @@ -37,14 +30,14 @@ def __init__(self, # Seperate model per constraint. input_dim = obs_space.shape[0] output_dim = act_space.shape[0] - - # default 1 layer + + # default 1 layer if isinstance(hidden_dim, int): - hidden_dims=[hidden_dim] + hidden_dims = [hidden_dim] elif isinstance(hidden_dim, list): hidden_dims = hidden_dim else: - raise ValueError("hidden_dim can only be int or list.") + raise ValueError('hidden_dim can only be int or list.') self.constraint_models = nn.ModuleList([ # MLP(input_dim, output_dim, hidden_dims=[hidden_dim]) MLP(input_dim, output_dim, hidden_dims=hidden_dims) @@ -53,7 +46,7 @@ def __init__(self, # Constraint slack variables/values. assert slack is not None and isinstance(slack, (int, float, list)) if isinstance(slack, (int, float)): - slack = [slack]*obs_space.shape[0] + slack = [slack] * self.num_constraints self.slack = np.array(slack) # Optimizers. self.optimizers = [ @@ -64,50 +57,38 @@ def __init__(self, def to(self, device ): - """Puts agent to device. - - """ + '''Puts agent to device.''' self.constraint_models.to(device) def train(self): - """Sets training mode. - - """ + '''Sets training mode.''' self.constraint_models.train() def eval(self): - """Sets evaluation mode. - - """ + '''Sets evaluation mode.''' self.constraint_models.eval() def state_dict(self): - """Snapshots agent state. - - """ + '''Snapshots agent state.''' return { - "constraint_models": self.constraint_models.state_dict(), - "optimizers": [opt.state_dict() for opt in self.optimizers] + 'constraint_models': self.constraint_models.state_dict(), + 'optimizers': [opt.state_dict() for opt in self.optimizers] } def load_state_dict(self, state_dict ): - """Restores agent state. - - """ - self.constraint_models.load_state_dict(state_dict["constraint_models"]) - for i, opt_state_dict in enumerate(state_dict["optimizers"]): + '''Restores agent state.''' + self.constraint_models.load_state_dict(state_dict['constraint_models']) + for i, opt_state_dict in enumerate(state_dict['optimizers']): self.optimizers[i].load_state_dict(opt_state_dict) def compute_loss(self, batch ): - """Gets constraint value L2 loss for each constraint. - - """ - obs, act = batch["obs"].to(self.device), batch["act"].to(self.device) - c, c_next = batch["c"].to(self.device), batch["c_next"].to(self.device) + '''Gets constraint value L2 loss for each constraint.''' + obs, act = batch['obs'].to(self.device), batch['act'].to(self.device) + c, c_next = batch['c'].to(self.device), batch['c_next'].to(self.device) gs = [model(obs) for model in self.constraint_models] @@ -124,16 +105,14 @@ def compute_loss(self, return losses def update(self, batch): - """Updates the constraint models from data batch. - - """ + '''Updates the constraint models from data batch.''' losses = self.compute_loss(batch) for loss, opt in zip(losses, self.optimizers): opt.zero_grad() loss.backward() opt.step() results = { - "constraint_{}_loss".format(i): loss.item() + f'constraint_{i}_loss': loss.item() for i, loss in enumerate(losses) } return results @@ -143,7 +122,7 @@ def get_safe_action(self, act, c ): - """Does action projection with the trained safety layer. + '''Does action projection with the trained safety layer. According to Dalal 2018, this simple projection works when only 1 constraint at a time is active; for multiple active constriants, either resort to in-graph QP solver such as @@ -153,11 +132,10 @@ def get_safe_action(self, obs (torch.FloatTensor): observations, shape (B,O). act (torch.FloatTensor): actions, shape (B,A). c (torch.FloatTensor): constraints, shape (B,C). - + Returns: torch.FloatTensor: transformed/projected actions, shape (B,A). - - """ + ''' self.eval() # [(B,A)]_C g = [model(obs) for model in self.constraint_models] @@ -175,17 +153,17 @@ def get_safe_action(self, mult = F.relu(numer / denomin) # (B,) multipliers.append(mult) multipliers = torch.stack(multipliers, -1) # (B,C) - # Check assumption on at most 1 active constraint - # - as mentioned in the original paper, this simple, analytical solution of the safety layer only holds - # with this assumption; otherwise resort to a differentiable layer for solving constrained optimization, + # Check assumption on at most 1 active constraint + # - as mentioned in the original paper, this simple, analytical solution of the safety layer only holds + # with this assumption; otherwise resort to a differentiable layer for solving constrained optimization, # e.g. OptLayer or the differentiable MPC works; or alternatively combine multiple constraints to a single one. - # - if the assumption is not satisfied, the layer will try to address the worst violation from the + # - if the assumption is not satisfied, the layer will try to address the worst violation from the # the largest lagrange variable (with the use of `topk(..., 1)`) # - to check the assumption, check for each step in batch if |{i | \lambda_i > 0}| <= 1 if float(torch.gt(multipliers, 0).float().sum()) > multipliers.shape[0]: - warnings.warn("""Assumption of at most 1 active constraint per step is violated in the current batch, - the filtered action will alleviate the worst violation but do not guarantee - satisfaction of all constraints, are you sure to proceed?""") + warnings.warn('''Assumption of at most 1 active constraint per step is violated in the current batch, + the filtered action will alleviate the worst violation but do not guarantee + satisfaction of all constraints, are you sure to proceed?''') # Calculate correction, equation (6) from Dalal 2018. max_mult, max_idx = torch.topk(multipliers, 1, dim=-1) # (B,1) max_idx = max_idx.view(-1).tolist() # []_B @@ -198,15 +176,14 @@ def get_safe_action(self, class ConstraintBuffer(object): - """Storage for replay buffer during training. + '''Storage for replay buffer during training. Attributes: max_size (int): maximum size of the replay buffer. batch_size (int): number of samples (steps) per batch. scheme (dict): describs shape & other info of data to be stored. keys (list): names of all data from scheme. - - """ + ''' def __init__(self, obs_space, @@ -225,46 +202,39 @@ def __init__(self, act_dim = act_space.n N = max_size self.scheme = { - "obs": { - "vshape": (N, *obs_dim) + 'obs': { + 'vshape': (N, *obs_dim) }, - "act": { - "vshape": (N, act_dim) + 'act': { + 'vshape': (N, act_dim) }, - "c": { - "vshape": (N, num_constraints) + 'c': { + 'vshape': (N, num_constraints) }, - "c_next": { - "vshape": (N, num_constraints) + 'c_next': { + 'vshape': (N, num_constraints) } } self.keys = list(self.scheme.keys()) self.reset() def reset(self): - """Allocate space for containers. - - """ + '''Allocate space for containers.''' for k, info in self.scheme.items(): - assert "vshape" in info, "Scheme must define vshape for {}".format( - k) - vshape = info["vshape"] - dtype = info.get("dtype", np.float32) - init = info.get("init", np.zeros) + assert 'vshape' in info, f'Scheme must define vshape for {k}' + vshape = info['vshape'] + dtype = info.get('dtype', np.float32) + init = info.get('init', np.zeros) self.__dict__[k] = init(vshape, dtype=dtype) self.pos = 0 self.buffer_size = 0 def __len__(self): - """Returns current size of the buffer. - - """ + '''Returns current size of the buffer.''' return self.buffer_size def state_dict(self): - """Returns a snapshot of current buffer. - - """ + '''Returns a snapshot of current buffer.''' state = dict( pos=self.pos, buffer_size=self.buffer_size, @@ -277,24 +247,20 @@ def state_dict(self): def load_state_dict(self, state ): - """Restores buffer from previous state. - - """ + '''Restores buffer from previous state.''' for k, v in state.items(): self.__dict__[k] = v def push(self, batch ): - """Inserts transition step data (as dict) to storage. - - """ + '''Inserts transition step data (as dict) to storage.''' # Batch size. k = list(batch.keys())[0] n = batch[k].shape[0] for k, v in batch.items(): - shape = self.scheme[k]["vshape"][1:] - dtype = self.scheme[k].get("dtype", np.float32) + shape = self.scheme[k]['vshape'][1:] + dtype = self.scheme[k].get('dtype', np.float32) v_ = np.asarray(v, dtype=dtype).reshape((n,) + shape) if self.pos + n <= self.max_size: self.__dict__[k][self.pos:self.pos + n] = v_ @@ -310,23 +276,19 @@ def push(self, def sample(self, indices ): - """Returns partial data. - - """ + '''Returns partial data.''' batch = {} for k, info in self.scheme.items(): - shape = info["vshape"][1:] + shape = info['vshape'][1:] batch[k] = self.__dict__[k].reshape(-1, *shape)[indices] return batch def sampler(self, batch_size, - device="cpu", + device='cpu', drop_last=True ): - """Makes sampler to loop through all data. - - """ + '''Makes sampler to loop through all data.''' total_steps = len(self) sampler = random_sample(np.arange(total_steps), batch_size, drop_last) for indices in sampler: diff --git a/safe_control_gym/controllers/safe_explorer/safe_ppo.py b/safe_control_gym/controllers/safe_explorer/safe_ppo.py index 0bad15955..f34dea192 100644 --- a/safe_control_gym/controllers/safe_explorer/safe_ppo.py +++ b/safe_control_gym/controllers/safe_explorer/safe_ppo.py @@ -1,35 +1,33 @@ -"""PPO-based safe explorer. +'''PPO-based safe explorer.''' -""" import os import time -import numpy as np -import torch from collections import defaultdict -from safe_control_gym.utils.logging import ExperimentLogger -from safe_control_gym.utils.utils import get_random_state, set_random_state, is_wrapped -from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs -from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs -from safe_control_gym.envs.env_wrappers.record_episode_statistics import RecordEpisodeStatistics, VecRecordEpisodeStatistics -from safe_control_gym.math_and_models.normalization import BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer +import numpy as np +import torch from safe_control_gym.controllers.base_controller import BaseController from safe_control_gym.controllers.ppo.ppo_utils import compute_returns_and_advantages -from safe_control_gym.controllers.safe_explorer.safe_explorer_utils import SafetyLayer, ConstraintBuffer +from safe_control_gym.controllers.safe_explorer.safe_explorer_utils import ConstraintBuffer, SafetyLayer from safe_control_gym.controllers.safe_explorer.safe_ppo_utils import SafePPOAgent, SafePPOBuffer +from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics, + VecRecordEpisodeStatistics) +from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs +from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer, + RewardStdNormalizer) +from safe_control_gym.utils.logging import ExperimentLogger +from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state class SafeExplorerPPO(BaseController): - """Safety layer for constraint satisfaction for RL. - - """ + '''Safety layer for constraint satisfaction for RL.''' def __init__(self, env_func, training=True, - checkpoint_path="model_latest.pt", - output_dir="temp", + checkpoint_path='model_latest.pt', + output_dir='temp', use_gpu=False, seed=0, **kwargs @@ -42,12 +40,12 @@ def __init__(self, self.env = VecRecordEpisodeStatistics(self.env, self.deque_size) self.eval_env = env_func(seed=seed * 111) self.eval_env = RecordEpisodeStatistics(self.eval_env, self.deque_size) - self.num_constraints = self.env.envs[0].num_constraints + self.num_constraints = self.eval_env.constraints.num_state_constraints else: # Testing only. self.env = env_func() self.env = RecordEpisodeStatistics(self.env) - self.num_constraints = self.env.num_constraints + self.num_constraints = self.env.constraints.num_state_constraints # Safety layer. self.safety_layer = SafetyLayer(self.env.observation_space, self.env.action_space, @@ -90,100 +88,91 @@ def __init__(self, self.logger = ExperimentLogger(output_dir, log_file_out=log_file_out, use_tensorboard=use_tensorboard) def reset(self): - """Do initializations for training or evaluation. - - """ + '''Do initializations for training or evaluation.''' if self.training: if self.pretraining: self.constraint_buffer = ConstraintBuffer(self.env.observation_space, self.env.action_space, self.num_constraints, self.constraint_buffer_size) else: # Load safety layer for 2nd stage training. - assert self.pretrained, "Must provide a pre-trained model for adaptation." + assert self.pretrained, 'Must provide a pre-trained model for adaptation.' if os.path.isdir(self.pretrained): - self.pretrained = os.path.join(self.pretrained, "model_latest.pt") + self.pretrained = os.path.join(self.pretrained, 'model_latest.pt') state = torch.load(self.pretrained) - self.safety_layer.load_state_dict(state["safety_layer"]) + self.safety_layer.load_state_dict(state['safety_layer']) # Set up stats tracking. - self.env.add_tracker("constraint_violation", 0) - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("constraint_violation", 0, mode="queue") - self.eval_env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0) + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('constraint_violation', 0, mode='queue') + self.eval_env.add_tracker('mse', 0, mode='queue') self.total_steps = 0 obs, info = self.env.reset() self.obs = self.obs_normalizer(obs) - self.c = np.array([inf["constraint_values"] for inf in info["n"]]) + self.c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) else: # Add episodic stats to be tracked. - self.env.add_tracker("constraint_violation", 0, mode="queue") - self.env.add_tracker("constraint_values", 0, mode="queue") - self.env.add_tracker("mse", 0, mode="queue") + self.env.add_tracker('constraint_violation', 0, mode='queue') + self.env.add_tracker('constraint_values', 0, mode='queue') + self.env.add_tracker('mse', 0, mode='queue') def close(self): - """Shuts down and cleans up lingering resources. - - """ + '''Shuts down and cleans up lingering resources.''' self.env.close() if self.training: self.eval_env.close() self.logger.close() def save(self, - path + path, + save_buffer=False ): - """Saves model params and experiment state to checkpoint path. - - """ + '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) os.makedirs(path_dir, exist_ok=True) state_dict = { - "agent": self.agent.state_dict(), - "safety_layer": self.safety_layer.state_dict(), - "obs_normalizer": self.obs_normalizer.state_dict(), - "reward_normalizer": self.reward_normalizer.state_dict(), + 'agent': self.agent.state_dict(), + 'safety_layer': self.safety_layer.state_dict(), + 'obs_normalizer': self.obs_normalizer.state_dict(), + 'reward_normalizer': self.reward_normalizer.state_dict(), } - if self.training: + if self.training and save_buffer: exp_state = { - "total_steps": self.total_steps, - "obs": self.obs, - "c": self.c, - "random_state": get_random_state(), - "env_random_state": self.env.get_env_random_state() + 'total_steps': self.total_steps, + 'obs': self.obs, + 'c': self.c, + 'random_state': get_random_state(), + 'env_random_state': self.env.get_env_random_state() } state_dict.update(exp_state) if self.pretraining: - state_dict["constraint_buffer"] = self.constraint_buffer.state_dict() + state_dict['constraint_buffer'] = self.constraint_buffer.state_dict() torch.save(state_dict, path) def load(self, path ): - """Restores model and experiment given checkpoint path. - - """ + '''Restores model and experiment given checkpoint path.''' state = torch.load(path) # Restore policy. - self.agent.load_state_dict(state["agent"]) - self.safety_layer.load_state_dict(state["safety_layer"]) - self.obs_normalizer.load_state_dict(state["obs_normalizer"]) - self.reward_normalizer.load_state_dict(state["reward_normalizer"]) + self.agent.load_state_dict(state['agent']) + self.safety_layer.load_state_dict(state['safety_layer']) + self.obs_normalizer.load_state_dict(state['obs_normalizer']) + self.reward_normalizer.load_state_dict(state['reward_normalizer']) # Restore experiment state. if self.training: - self.total_steps = state["total_steps"] - self.obs = state["obs"] - self.c = state["c"] - set_random_state(state["random_state"]) - self.env.set_env_random_state(state["env_random_state"]) + self.total_steps = state['total_steps'] + self.obs = state['obs'] + self.c = state['c'] + set_random_state(state['random_state']) + self.env.set_env_random_state(state['env_random_state']) self.logger.load(self.total_steps) if self.pretraining: - self.constraint_buffer.load_state_dict(state["constraint_buffer"]) + self.constraint_buffer.load_state_dict(state['constraint_buffer']) def learn(self, env=None, **kwargs ): - """Performs learning (pre-training, training, fine-tuning, etc). - - """ + '''Performs learning (pre-training, training, fine-tuning, etc).''' if self.pretraining: final_step = self.constraint_epochs train_func = self.pretrain_step @@ -195,44 +184,59 @@ def learn(self, # Checkpoint. if self.total_steps >= final_step or (self.save_interval and self.total_steps % self.save_interval == 0): # Latest/final checkpoint. - self.save(self.checkpoint_path) - self.logger.info("Checkpoint | {}".format(self.checkpoint_path)) + self.save(self.checkpoint_path, save_buffer=False) + self.logger.info(f'Checkpoint | {self.checkpoint_path}') if self.num_checkpoints and self.total_steps % (final_step // self.num_checkpoints) == 0: # Intermediate checkpoint. - path = os.path.join(self.output_dir, "checkpoints", "model_{}.pt".format(self.total_steps)) - self.save(path) + path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') + self.save(path, save_buffer=True) # Evaluation. if self.eval_interval and self.total_steps % self.eval_interval == 0: if self.pretraining: eval_results = self.eval_constraint_models() - results["eval"] = eval_results + results['eval'] = eval_results else: eval_results = self.run(env=self.eval_env, n_episodes=self.eval_batch_size) - results["eval"] = eval_results - self.logger.info("Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}".format(eval_results["ep_lengths"].mean(), - eval_results["ep_lengths"].std(), - eval_results["ep_returns"].mean(), - eval_results["ep_returns"].std())) + results['eval'] = eval_results + self.logger.info('Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}'.format(eval_results['ep_lengths'].mean(), + eval_results['ep_lengths'].std(), + eval_results['ep_returns'].mean(), + eval_results['ep_returns'].std())) # Save the best model. - eval_score = eval_results["ep_returns"].mean() - eval_best_score = getattr(self, "eval_best_score", -np.infty) + eval_score = eval_results['ep_returns'].mean() + eval_best_score = getattr(self, 'eval_best_score', -np.infty) if self.eval_save_best and eval_best_score < eval_score: self.eval_best_score = eval_score - self.save(os.path.join(self.output_dir, "model_best.pt")) + self.save(os.path.join(self.output_dir, 'model_best.pt'), save_buffer=False) # Logging. if self.log_interval and self.total_steps % self.log_interval == 0: self.log_step(results) + def select_action(self, obs, info=None): + '''Determine the action to take at the current timestep. + + Args: + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. + + Returns: + action (ndarray): The action chosen by the controller. + ''' + c = info['constraint_values'] + with torch.no_grad(): + obs = torch.FloatTensor(obs).to(self.device) + c = torch.FloatTensor(c).to(self.device) + action = self.agent.ac.act(obs, c=c) + + return action + def run(self, env=None, render=False, n_episodes=10, verbose=False, - **kwargs ): - """Runs evaluation with current policy. - - """ + '''Runs evaluation with current policy.''' self.agent.eval() self.obs_normalizer.set_read_only() if env is None: @@ -240,38 +244,33 @@ def run(self, else: if not is_wrapped(env, RecordEpisodeStatistics): env = RecordEpisodeStatistics(env, n_episodes) - env.add_tracker("constraint_violation", 0, mode="queue") - env.add_tracker("constraint_values", 0, mode="queue") - env.add_tracker("mse", 0, mode="queue") + env.add_tracker('constraint_violation', 0, mode='queue') + env.add_tracker('constraint_values', 0, mode='queue') + env.add_tracker('mse', 0, mode='queue') obs, info = env.reset() obs = self.obs_normalizer(obs) - c = info["constraint_values"] ep_returns, ep_lengths = [], [] frames = [] while len(ep_returns) < n_episodes: - with torch.no_grad(): - obs = torch.FloatTensor(obs).to(self.device) - c = torch.FloatTensor(c).to(self.device) - action = self.agent.ac.act(obs, c=c) - obs, reward, done, info = env.step(action) + action = self.select_action(obs, info) + obs, _, done, info = env.step(action) if render: env.render() - frames.append(env.render("rgb_array")) + frames.append(env.render('rgb_array')) if verbose: - print("obs {} | act {}".format(obs, action)) + print(f'obs {obs} | act {action}') if done: - assert "episode" in info - ep_returns.append(info["episode"]["r"]) - ep_lengths.append(info["episode"]["l"]) + assert 'episode' in info + ep_returns.append(info['episode']['r']) + ep_lengths.append(info['episode']['l']) obs, info = env.reset() obs = self.obs_normalizer(obs) - c = info["constraint_values"] # Collect evaluation results. ep_lengths = np.asarray(ep_lengths) ep_returns = np.asarray(ep_returns) - eval_results = {"ep_returns": ep_returns, "ep_lengths": ep_lengths} + eval_results = {'ep_returns': ep_returns, 'ep_lengths': ep_lengths} if len(frames) > 0: - eval_results["frames"] = frames + eval_results['frames'] = frames # Other episodic stats from evaluation env. if len(env.queued_stats) > 0: queued_stats = {k: np.asarray(v) for k, v in env.queued_stats.items()} @@ -279,9 +278,7 @@ def run(self, return eval_results def pretrain_step(self): - """Performs a pre-trianing step. - - """ + '''Performs a pre-trianing step.''' results = defaultdict(list) start = time.time() self.safety_layer.train() @@ -296,13 +293,11 @@ def pretrain_step(self): results[k].append(v) self.constraint_buffer.reset() results = {k: sum(v) / len(v) for k, v in results.items()} - results.update({"step": self.total_steps, "elapsed_time": time.time() - start}) + results.update({'step': self.total_steps, 'elapsed_time': time.time() - start}) return results def train_step(self): - """Performs a training/fine-tuning step. - - """ + '''Performs a training/fine-tuning step.''' self.agent.train() self.obs_normalizer.unset_read_only() rollouts = SafePPOBuffer(self.env.observation_space, @@ -313,7 +308,7 @@ def train_step(self): obs = self.obs c = self.c start = time.time() - for step in range(self.rollout_steps): + for _ in range(self.rollout_steps): with torch.no_grad(): act, v, logp = self.agent.ac.step(torch.FloatTensor(obs).to(self.device), c=torch.FloatTensor(c).to(self.device)) next_obs, rew, done, info = self.env.step(act) @@ -322,27 +317,27 @@ def train_step(self): mask = 1 - done.astype(float) # Time truncation is not the same as the true termination. terminal_v = np.zeros_like(v) - for idx, inf in enumerate(info["n"]): - if "terminal_info" not in inf: + for idx, inf in enumerate(info['n']): + if 'terminal_info' not in inf: continue - inff = inf["terminal_info"] - if "TimeLimit.truncated" in inff and inff["TimeLimit.truncated"]: - terminal_obs = inf["terminal_observation"] + inff = inf['terminal_info'] + if 'TimeLimit.truncated' in inff and inff['TimeLimit.truncated']: + terminal_obs = inf['terminal_observation'] terminal_obs_tensor = torch.FloatTensor(terminal_obs).unsqueeze(0).to(self.device) terminal_val = self.agent.ac.critic(terminal_obs_tensor).squeeze().detach().numpy() terminal_v[idx] = terminal_val rollouts.push({ - "obs": obs, - "act": act, - "rew": rew, - "mask": mask, - "v": v, - "logp": logp, - "terminal_v": terminal_v, - "c": c, + 'obs': obs, + 'act': act, + 'rew': rew, + 'mask': mask, + 'v': v, + 'logp': logp, + 'terminal_v': terminal_v, + 'c': c, }) obs = next_obs - c = np.array([inf["constraint_values"] for inf in info["n"]]) + c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) self.obs = obs self.c = c self.total_steps += self.rollout_batch_size * self.rollout_steps @@ -360,111 +355,105 @@ def train_step(self): # Prevent divide-by-0 for repetitive tasks. rollouts.adv = (adv - adv.mean()) / (adv.std() + 1e-6) results = self.agent.update(rollouts, self.device) - results.update({"step": self.total_steps, "elapsed_time": time.time() - start}) + results.update({'step': self.total_steps, 'elapsed_time': time.time() - start}) return results def log_step(self, results ): - """Does logging after a training step. - - """ - step = results["step"] + '''Does logging after a training step.''' + step = results['step'] final_step = self.constraint_epochs if self.pretraining else self.max_env_steps # Runner stats. self.logger.add_scalars( { - "step": step, - "time": results["elapsed_time"], - "progress": step / final_step + 'step': step, + 'time': results['elapsed_time'], + 'progress': step / final_step }, step, - prefix="time", + prefix='time', write=False, write_tb=False) if self.pretraining: # Constraint learning stats. for i in range(self.safety_layer.num_constraints): - name = "constraint_{}_loss".format(i) - self.logger.add_scalars({name: results[name]}, step, prefix="constraint_loss") - if "eval" in results: - self.logger.add_scalars({name: results["eval"][name]}, step, prefix="constraint_loss_eval") + name = f'constraint_{i}_loss' + self.logger.add_scalars({name: results[name]}, step, prefix='constraint_loss') + if 'eval' in results: + self.logger.add_scalars({name: results['eval'][name]}, step, prefix='constraint_loss_eval') else: # Learning stats. self.logger.add_scalars( { - k: results[k] - for k in ["policy_loss", "value_loss", "entropy_loss", "approx_kl"] - }, - step, - prefix="loss") + k: results[k] + for k in ['policy_loss', 'value_loss', 'entropy_loss', 'approx_kl'] + }, + step, + prefix='loss') # Performance stats. ep_lengths = np.asarray(self.env.length_queue) ep_returns = np.asarray(self.env.return_queue) - ep_constraint_violation = np.asarray(self.env.queued_stats["constraint_violation"]) + ep_constraint_violation = np.asarray(self.env.queued_stats['constraint_violation']) self.logger.add_scalars( { - "ep_length": ep_lengths.mean(), - "ep_return": ep_returns.mean(), - "ep_reward": (ep_returns / ep_lengths).mean(), - "ep_constraint_violation": ep_constraint_violation.mean() + 'ep_length': ep_lengths.mean(), + 'ep_return': ep_returns.mean(), + 'ep_reward': (ep_returns / ep_lengths).mean(), + 'ep_constraint_violation': ep_constraint_violation.mean() }, step, - prefix="stat") + prefix='stat') # Total constraint violation during learning. - total_violations = self.env.accumulated_stats["constraint_violation"] - self.logger.add_scalars({"constraint_violation": total_violations}, step, prefix="stat") - if "eval" in results: - eval_ep_lengths = results["eval"]["ep_lengths"] - eval_ep_returns = results["eval"]["ep_returns"] - eval_constraint_violation = results["eval"]["constraint_violation"] - eval_mse = results["eval"]["mse"] + total_violations = self.env.accumulated_stats['constraint_violation'] + self.logger.add_scalars({'constraint_violation': total_violations}, step, prefix='stat') + if 'eval' in results: + eval_ep_lengths = results['eval']['ep_lengths'] + eval_ep_returns = results['eval']['ep_returns'] + eval_constraint_violation = results['eval']['constraint_violation'] + eval_mse = results['eval']['mse'] self.logger.add_scalars( { - "ep_length": eval_ep_lengths.mean(), - "ep_return": eval_ep_returns.mean(), - "ep_reward": (eval_ep_returns / eval_ep_lengths).mean(), - "constraint_violation": eval_constraint_violation.mean(), - "mse": eval_mse.mean() + 'ep_length': eval_ep_lengths.mean(), + 'ep_return': eval_ep_returns.mean(), + 'ep_reward': (eval_ep_returns / eval_ep_lengths).mean(), + 'constraint_violation': eval_constraint_violation.mean(), + 'mse': eval_mse.mean() }, step, - prefix="stat_eval") + prefix='stat_eval') # Print summary table. self.logger.dump_scalars() def collect_constraint_data(self, num_steps ): - """Uses random policy to collect data for pre-training constriant models. - - """ + '''Uses random policy to collect data for pre-training constriant models.''' step = 0 obs, info = self.env.reset() obs = self.obs_normalizer(obs) - c = np.array([inf["constraint_values"] for inf in info["n"]]) + c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) while step < num_steps: - action_spaces = self.env.get_attr("action_space") + action_spaces = self.env.get_attr('action_space') action = np.array([space.sample() for space in action_spaces]) obs_next, _, done, info = self.env.step(action) obs_next = self.obs_normalizer(obs_next) c_next = [] for i, d in enumerate(done): if d: - c_next_i = info["n"][i]["terminal_info"]["constraint_values"] + c_next_i = info['n'][i]['terminal_info']['constraint_values'][:self.num_constraints] else: - c_next_i = info["n"][i]["constraint_values"] + c_next_i = info['n'][i]['constraint_values'][:self.num_constraints] c_next.append(c_next_i) c_next = np.array(c_next) - self.constraint_buffer.push({"act": action, "obs": obs, "c": c, "c_next": c_next}) + self.constraint_buffer.push({'act': action, 'obs': obs, 'c': c, 'c_next': c_next}) obs = obs_next - c = np.array([inf["constraint_values"] for inf in info["n"]]) + c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) step += self.rollout_batch_size def eval_constraint_models(self): - """Runs evaluation for the constraint models. - - """ - eval_resutls = defaultdict(list) + '''Runs evaluation for the constraint models.''' + eval_results = defaultdict(list) self.safety_layer.eval() self.obs_normalizer.set_read_only() # Collect evaluation data. @@ -472,7 +461,7 @@ def eval_constraint_models(self): for batch in self.constraint_buffer.sampler(self.constraint_batch_size): losses = self.safety_layer.compute_loss(batch) for i, loss in enumerate(losses): - eval_resutls["constraint_{}_loss".format(i)].append(loss.item()) + eval_results[f'constraint_{i}_loss'].append(loss.item()) self.constraint_buffer.reset() - eval_resutls = {k: sum(v) / len(v) for k, v in eval_resutls.items()} - return eval_resutls + eval_results = {k: sum(v) / len(v) for k, v in eval_results.items()} + return eval_results diff --git a/safe_control_gym/controllers/safe_explorer/safe_ppo.yaml b/safe_control_gym/controllers/safe_explorer/safe_ppo.yaml index c82b355b2..771e57b2c 100644 --- a/safe_control_gym/controllers/safe_explorer/safe_ppo.yaml +++ b/safe_control_gym/controllers/safe_explorer/safe_ppo.yaml @@ -6,7 +6,7 @@ clip_obs: 10. clip_reward: 10. # Safety layer args -pretraining: True +pretraining: True pretrained: null constraint_hidden_dim: 10 constraint_lr: 0.0001 @@ -47,5 +47,5 @@ log_interval: 0 save_interval: 0 num_checkpoints: 0 eval_interval: 0 -eval_save_best: False +eval_save_best: False tensorboard: False diff --git a/safe_control_gym/controllers/safe_explorer/safe_ppo_utils.py b/safe_control_gym/controllers/safe_explorer/safe_ppo_utils.py index 79bf561c7..a08529c31 100644 --- a/safe_control_gym/controllers/safe_explorer/safe_ppo_utils.py +++ b/safe_control_gym/controllers/safe_explorer/safe_ppo_utils.py @@ -1,21 +1,17 @@ -"""Utility function for the PPO-based safe explorer. +'''Utility function for the PPO-based safe explorer.''' -""" import numpy as np import torch import torch.nn as nn +from gymnasium.spaces import Box -from gym.spaces import Box - -from safe_control_gym.math_and_models.neural_networks import MLP, CNN, RNN, init_ -from safe_control_gym.math_and_models.distributions import Normal, Categorical import safe_control_gym.controllers.ppo.ppo_utils as ppo_utils +from safe_control_gym.math_and_models.distributions import Categorical, Normal +from safe_control_gym.math_and_models.neural_networks import MLP class SafePPOAgent(ppo_utils.PPOAgent): - """A PPO class that encapsulates models, optimizers and update functions. - - """ + '''A PPO class that encapsulates models, optimizers and update functions.''' def __init__(self, obs_space, @@ -45,17 +41,15 @@ def __init__(self, self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, - activation="tanh", + activation='tanh', action_modifier=action_modifier) # Optimizers. self.actor_opt = torch.optim.Adam(self.ac.actor.parameters(), actor_lr) self.critic_opt = torch.optim.Adam(self.ac.critic.parameters(), critic_lr) def compute_policy_loss(self, batch): - """Returns policy loss(es) given batch of data. - - """ - obs, act, logp_old, adv, c = batch["obs"], batch["act"], batch["logp"], batch["adv"], batch["c"] + '''Returns policy loss(es) given batch of data.''' + obs, act, logp_old, adv, c = batch['obs'], batch['act'], batch['logp'], batch['adv'], batch['c'] dist, logp = self.ac.actor(obs, act, c=c) # Policy. ratio = torch.exp(logp - logp_old) @@ -69,9 +63,7 @@ def compute_policy_loss(self, batch): class MLPActor(nn.Module): - """Actor model. - - """ + '''Actor model.''' def __init__(self, obs_dim, @@ -81,9 +73,6 @@ def __init__(self, discrete=False, action_modifier=None ): - """ - - """ super().__init__() self.pi_net = MLP(obs_dim, act_dim, hidden_dims, activation) # Construct output action distribution. @@ -101,9 +90,6 @@ def forward(self, act=None, c=None ): - """ - - """ mu = self.pi_net(obs) # Filter action if needed. if self.action_modifier: @@ -125,24 +111,20 @@ def forward(self, class MLPActorCritic(ppo_utils.MLPActorCritic): - """Model for the actor-critic agent. + '''Model for the actor-critic agent. Attributes: - actor (MLPActor): policy network. - critic (MLPCritic): value network. - - """ + actor (MLPActor): policy network. + critic (MLPCritic): value network. + ''' def __init__(self, obs_space, act_space, hidden_dims=(64, 64), - activation="tanh", + activation='tanh', action_modifier=None ): - """ - - """ nn.Module.__init__(self) obs_dim = obs_space.shape[0] if isinstance(act_space, Box): @@ -160,9 +142,6 @@ def step(self, obs, c=None ): - """ - - """ dist, _ = self.actor(obs, c=c) a = dist.sample() logp_a = dist.log_prob(a) @@ -173,24 +152,20 @@ def act(self, obs, c=None ): - """ - - """ dist, _ = self.actor(obs, c=c) a = dist.mode() return a.numpy() class SafePPOBuffer(ppo_utils.PPOBuffer): - """Storage for a batch of episodes during training. - + '''Storage for a batch of episodes during training. + Attributes: max_length (int): maximum length of episode. batch_size (int): number of episodes per batch. scheme (dict): describs shape & other info of data to be stored. keys (list): names of all data from scheme. - - """ + ''' def __init__(self, obs_space, @@ -199,9 +174,6 @@ def __init__(self, max_length, batch_size ): - """ - - """ self.max_length = max_length self.batch_size = batch_size T, N = max_length, batch_size @@ -211,36 +183,36 @@ def __init__(self, else: act_dim = act_space.n self.scheme = { - "obs": { - "vshape": (T, N, *obs_dim) + 'obs': { + 'vshape': (T, N, *obs_dim) }, - "act": { - "vshape": (T, N, act_dim) + 'act': { + 'vshape': (T, N, act_dim) }, - "rew": { - "vshape": (T, N, 1) + 'rew': { + 'vshape': (T, N, 1) }, - "mask": { - "vshape": (T, N, 1), - "init": np.ones + 'mask': { + 'vshape': (T, N, 1), + 'init': np.ones }, - "v": { - "vshape": (T, N, 1) + 'v': { + 'vshape': (T, N, 1) }, - "logp": { - "vshape": (T, N, 1) + 'logp': { + 'vshape': (T, N, 1) }, - "ret": { - "vshape": (T, N, 1) + 'ret': { + 'vshape': (T, N, 1) }, - "adv": { - "vshape": (T, N, 1) + 'adv': { + 'vshape': (T, N, 1) }, - "terminal_v": { - "vshape": (T, N, 1) + 'terminal_v': { + 'vshape': (T, N, 1) }, - "c": { - "vshape": (T, N, num_constraints) + 'c': { + 'vshape': (T, N, num_constraints) }, } self.keys = list(self.scheme.keys()) diff --git a/safe_control_gym/envs/disturbances.py b/safe_control_gym/envs/disturbances.py index 27be3f170..2f58b8f03 100644 --- a/safe_control_gym/envs/disturbances.py +++ b/safe_control_gym/envs/disturbances.py @@ -1,14 +1,10 @@ -"""Disturbances. +'''Disturbances.''' -""" -from enum import Enum import numpy as np class Disturbance: - """Base class for disturbance or noise applied to inputs or dyanmics. - - """ + '''Base class for disturbance or noise applied to inputs or dyanmics.''' def __init__(self, env, @@ -31,38 +27,27 @@ def apply(self, target, env ): - """Default is identity. - - """ + '''Default is identity.''' return target - + def seed(self, env): - """Reset seed from env. - - """ + '''Reset seed from env.''' self.np_random = env.np_random - class DisturbanceList: - """Combine list of disturbances as one. - - """ + '''Combine list of disturbances as one.''' def __init__(self, disturbances ): - """Initialization of the list of disturbances. - - """ + '''Initialization of the list of disturbances.''' self.disturbances = disturbances def reset(self, env ): - """Sequentially reset disturbances. - - """ + '''Sequentially reset disturbances.''' for disturb in self.disturbances: disturb.reset(env) @@ -70,31 +55,26 @@ def apply(self, target, env ): - """Sequentially apply disturbances. - - """ + '''Sequentially apply disturbances.''' disturbed = target for disturb in self.disturbances: disturbed = disturb.apply(disturbed, env) return disturbed - + def seed(self, env): - """Reset seed from env. - - """ + '''Reset seed from env.''' for disturb in self.disturbances: disturb.seed(env) class ImpulseDisturbance(Disturbance): - """Impulse applied during a short time interval. - + '''Impulse applied during a short time interval. + Examples: * single step, square (duration=1, decay_rate=1): ______|-|_______ * multiple step, square (duration>1, decay_rate=1): ______|-----|_____ - * multiple step, triangle (duration>1, decay_rate<1): ______/\_____ - - """ + * multiple step, triangle (duration>1, decay_rate<1): ______/\\_____ + ''' def __init__(self, env, @@ -120,7 +100,7 @@ def reset(self, env ): if self.step_offset is None: - self.current_step_offset = self.np_random.randint(self.max_step) + self.current_step_offset = self.np_random.integers(self.max_step) else: self.current_step_offset = self.step_offset self.current_peak_step = int(self.current_step_offset + self.duration / 2) @@ -144,11 +124,10 @@ def apply(self, class StepDisturbance(Disturbance): - """Constant disturbance at all time steps (but after offset). - - Applied after offset step (randomized or given): _______|--------- + '''Constant disturbance at all time steps (but after offset). - """ + Applied after offset step (randomized or given): _______|--------- + ''' def __init__(self, env, @@ -167,7 +146,7 @@ def reset(self, env ): if self.step_offset is None: - self.current_step_offset = self.np_random.randint(self.max_step) + self.current_step_offset = self.np_random.integers(self.max_step) else: self.current_step_offset = self.step_offset @@ -183,8 +162,9 @@ def apply(self, disturbed = target + noise return disturbed + class UniformNoise(Disturbance): - """i.i.d uniform noise ~ U(low, high) per time step.""" + '''i.i.d uniform noise ~ U(low, high) per time step.''' def __init__(self, env, dim, mask=None, low=0.0, high=1.0, **kwargs): super().__init__(env, dim, mask) @@ -195,14 +175,14 @@ def __init__(self, env, dim, mask=None, low=0.0, high=1.0, **kwargs): elif isinstance(low, list): self.low = np.asarray(low) else: - raise ValueError("[ERROR] UniformNoise.__init__(): low must be specified as a float or list.") + raise ValueError('[ERROR] UniformNoise.__init__(): low must be specified as a float or list.') if isinstance(high, float): self.high = np.asarray([high] * self.dim) elif isinstance(low, list): self.high = np.asarray(high) else: - raise ValueError("[ERROR] UniformNoise.__init__(): high must be specified as a float or list.") + raise ValueError('[ERROR] UniformNoise.__init__(): high must be specified as a float or list.') def apply(self, target, env): noise = self.np_random.uniform(self.low, self.high, size=self.dim) @@ -213,9 +193,7 @@ def apply(self, target, env): class WhiteNoise(Disturbance): - """I.i.d Gaussian noise per time step. - - """ + '''I.i.d Gaussian noise per time step.''' def __init__(self, env, @@ -231,17 +209,14 @@ def __init__(self, elif isinstance(std, list): self.std = np.asarray(std) else: - raise ValueError("[ERROR] WhiteNoise.__init__(): std must be specified as a float or list.") - assert self.dim == len(self.std), "std shape should be the same as dim." + raise ValueError('[ERROR] WhiteNoise.__init__(): std must be specified as a float or list.') + assert self.dim == len(self.std), 'std shape should be the same as dim.' def apply(self, target, env ): noise = self.np_random.normal(0, self.std, size=self.dim) - # # TODO: hack for debug - # noise = np.clip(noise, -self.std, self.std) - if self.mask is not None: noise *= self.mask disturbed = target + noise @@ -249,18 +224,14 @@ def apply(self, class BrownianNoise(Disturbance): - """Simple random walk noise. - - """ + '''Simple random walk noise.''' def __init__(self): super().__init__() class PeriodicNoise(Disturbance): - """Sinuisodal noise. - - """ + '''Sinuisodal noise.''' def __init__(self, env, @@ -289,11 +260,10 @@ def apply(self, class StateDependentDisturbance(Disturbance): - """Time varying and state varying, e.g. friction. - - Here to provide an explicit form, can also enable friction in simulator directly. + '''Time varying and state varying, e.g. friction. - """ + Here to provide an explicit form, can also enable friction in simulator directly. + ''' def __init__(self, env, @@ -304,32 +274,30 @@ def __init__(self, super().__init__() -DISTURBANCE_TYPES = {"impulse": ImpulseDisturbance, - "step": StepDisturbance, - "uniform": UniformNoise, - "white_noise": WhiteNoise, - "periodic": PeriodicNoise, +DISTURBANCE_TYPES = {'impulse': ImpulseDisturbance, + 'step': StepDisturbance, + 'uniform': UniformNoise, + 'white_noise': WhiteNoise, + 'periodic': PeriodicNoise, } def create_disturbance_list(disturbance_specs, shared_args, env): - """Creates a DisturbanceList from yaml disturbance specification. + '''Creates a DisturbanceList from yaml disturbance specification. Args: disturbance_specs (list): List of dicts defining the disturbances info. shared_args (dict): args shared across the disturbances in the list. env (BenchmarkEnv): Env for which the constraints will be applied - """ + ''' disturb_list = [] # Each disturbance for the mode. for disturb in disturbance_specs: - assert "disturbance_func" in disturb.keys(), "[ERROR]: Every distrubance must specify a disturbance_func." - disturb_func = disturb["disturbance_func"] - assert disturb_func in DISTURBANCE_TYPES, "[ERROR] in BenchmarkEnv._setup_disturbances(), disturbance type not available." + assert 'disturbance_func' in disturb.keys(), '[ERROR]: Every distrubance must specify a disturbance_func.' + disturb_func = disturb['disturbance_func'] + assert disturb_func in DISTURBANCE_TYPES, '[ERROR] in BenchmarkEnv._setup_disturbances(), disturbance type not available.' disturb_cls = DISTURBANCE_TYPES[disturb_func] - cfg = {key: disturb[key] for key in disturb if key != "disturbance_func"} + cfg = {key: disturb[key] for key in disturb if key != 'disturbance_func'} disturb = disturb_cls(env, **shared_args, **cfg) disturb_list.append(disturb) return DisturbanceList(disturb_list) - - diff --git a/safe_control_gym/envs/env_wrappers/record_episode_statistics.py b/safe_control_gym/envs/env_wrappers/record_episode_statistics.py index 26673696d..e2300dbae 100644 --- a/safe_control_gym/envs/env_wrappers/record_episode_statistics.py +++ b/safe_control_gym/envs/env_wrappers/record_episode_statistics.py @@ -1,19 +1,20 @@ -import time -import gym -import numpy as np +'''Record Episode Statistics.''' +import time from collections import deque from copy import deepcopy +import gymnasium as gym +import numpy as np + from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env import VecEnvWrapper class RecordEpisodeStatistics(gym.Wrapper): - """ Keep track of episode length and returns per instantiated env + '''Keep track of episode length and returns per instantiated env - Based on OpenAI's Gym wrapper record_episode_statistics.py - - """ + Based on OpenAI's Gym wrapper record_episode_statistics.py + ''' def __init__(self, env, @@ -35,22 +36,21 @@ def __init__(self, def add_tracker(self, name, init_value, - mode="accumulate" + mode='accumulate' ): - """Adds a specific stat to be tracked (accumulate|queue). - + '''Adds a specific stat to be tracked (accumulate|queue). + Modes to track stats * accumulate: rolling sum, e.g. total # of constraint violations during training. * queue: finite, individual storage, e.g. returns, lengths, constraint costs. - - """ + ''' self.episode_stats[name] = init_value - if mode == "accumulate": + if mode == 'accumulate': self.accumulated_stats[name] = init_value - elif mode == "queue": + elif mode == 'queue': self.queued_stats[name] = deque(maxlen=self.deque_size) else: - raise Exception("Tracker mode not implemented.") + raise Exception('Tracker mode not implemented.') def reset(self, **kwargs @@ -90,11 +90,10 @@ def step(self, class VecRecordEpisodeStatistics(VecEnvWrapper): - """ A vectorized wrapper that records episodic statistics. + '''A vectorized wrapper that records episodic statistics. E.g. episode lengths, returns, constraint violations. - - """ + ''' def __init__(self, venv, @@ -115,18 +114,16 @@ def __init__(self, def add_tracker(self, name, init_value, - mode="accumulate" + mode='accumulate' ): - """Adds a specific stat to be tracked (accumulated). - - """ + '''Adds a specific stat to be tracked (accumulated).''' self.episode_stats[name] = [init_value for _ in range(self.num_envs)] - if mode == "accumulate": + if mode == 'accumulate': self.accumulated_stats[name] = init_value - elif mode == "queue": + elif mode == 'queue': self.queued_stats[name] = deque(maxlen=self.deque_size) else: - raise Exception("Tracker mode not implemented.") + raise Exception('Tracker mode not implemented.') def reset(self, **kwargs @@ -147,20 +144,20 @@ def step_wait(self): # Add other tracked stats. for key in self.episode_stats: if d: - inf = info["n"][i]["terminal_info"] + inf = info['n'][i]['terminal_info'] else: - inf = info["n"][i] + inf = info['n'][i] if key in inf: self.episode_stats[key][i] += inf[key] if d: - info["n"][i]['episode'] = {'r': self.episode_return[i], 'l': self.episode_length[i]} + info['n'][i]['episode'] = {'r': self.episode_return[i], 'l': self.episode_length[i]} self.return_queue.append(deepcopy(self.episode_return[i])) self.length_queue.append(deepcopy(self.episode_length[i])) self.episode_return[i] = 0 self.episode_length[i] = 0 # Other tracked stats. for key in self.episode_stats: - info["n"][i]['episode'][key] = deepcopy(self.episode_stats[key][i]) + info['n'][i]['episode'][key] = deepcopy(self.episode_stats[key][i]) if key in self.accumulated_stats: self.accumulated_stats[key] += deepcopy(self.episode_stats[key][i]) if key in self.queued_stats: diff --git a/safe_control_gym/envs/env_wrappers/vectorized_env/__init__.py b/safe_control_gym/envs/env_wrappers/vectorized_env/__init__.py index dd775f0f8..7438fe43f 100644 --- a/safe_control_gym/envs/env_wrappers/vectorized_env/__init__.py +++ b/safe_control_gym/envs/env_wrappers/vectorized_env/__init__.py @@ -1,5 +1,8 @@ -import numpy as np +'''Initializing vectorized environments.''' + import random + +import numpy as np import torch from safe_control_gym.envs.env_wrappers.vectorized_env.dummy_vec_env import DummyVecEnv @@ -10,18 +13,17 @@ def make_env_fn(env_func, env_config, seed, rank): - """Higher-order function for env init func. + '''Higher-order function for env init func. Args: env_func (function): partial function that can accept args. env_config (dict): env init args. seed (int): base seed. rank (int): unique seed for each parallel env. - - Returns: - _thunk: env-constructing func. - """ + Returns: + _thunk (func): env-constructing func. + ''' def _thunk(): # Do not set seed i if 0 (e.g. for evaluation). @@ -42,19 +44,18 @@ def make_vec_envs(env_func, batch_size=1, n_processes=1, seed=None): - """Produce envs with parallel rollout abilities. + '''Produce envs with parallel rollout abilities. Args: env_func (function): partial function that can accept args. - env_config (dict): non-shareable args for each env. - batch_size (int): total num of parallel envs. + env_config (dict): non-shareable args for each env. + batch_size (int): total num of parallel envs. n_processes (int): num of parallel workers to run envs. - seed (int): base seed for the run. + seed (int): base seed for the run. Returns: - VecEnv: (wrapped) parallel envs. - - """ + VecEnv (Vectorized env): (wrapped) parallel envs. + ''' if env_configs is None: env_configs = [{}] * batch_size env_fns = [make_env_fn(env_func, env_configs[i], seed, i) for i in range(batch_size)] diff --git a/safe_control_gym/envs/env_wrappers/vectorized_env/dummy_vec_env.py b/safe_control_gym/envs/env_wrappers/vectorized_env/dummy_vec_env.py index 51727f37d..34870f1f4 100644 --- a/safe_control_gym/envs/env_wrappers/vectorized_env/dummy_vec_env.py +++ b/safe_control_gym/envs/env_wrappers/vectorized_env/dummy_vec_env.py @@ -1,22 +1,20 @@ +'''Dummy Vectorized Environments.''' + import copy + import numpy as np from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env import VecEnv -from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_list, _flatten_obs +from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs from safe_control_gym.utils.utils import get_random_state, set_random_state class DummyVecEnv(VecEnv): - """Single thread env (allow multiple envs sequentially). - - """ + '''Single thread env (allow multiple envs sequentially).''' def __init__(self, env_fns ): - """ - - """ self.envs = [fn() for fn in env_fns] env = self.envs[0] VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space) @@ -26,15 +24,9 @@ def __init__(self, def step_async(self, actions ): - """ - - """ self.actions = actions def step_wait(self): - """ - - """ results = [] for i in range(self.num_envs): obs, rew, done, info = self.envs[i].step(self.actions[i]) @@ -42,26 +34,20 @@ def step_wait(self): end_obs = copy.deepcopy(obs) end_info = copy.deepcopy(info) obs, info = self.envs[i].reset() - info["terminal_observation"] = end_obs - info["terminal_info"] = end_info + info['terminal_observation'] = end_obs + info['terminal_info'] = end_info results.append([obs, rew, done, info]) obs, rews, dones, infos = zip(*results) - return _flatten_obs(obs), np.array(rews), np.array(dones), {"n": infos} + return _flatten_obs(obs), np.array(rews), np.array(dones), {'n': infos} def reset(self): - """ - - """ results = [] for env in self.envs: results.append(env.reset()) obs, infos = zip(*results) - return _flatten_obs(obs), {"n": infos} + return _flatten_obs(obs), {'n': infos} def close(self): - """ - - """ for env in self.envs: env.close() if self.viewer is not None: @@ -69,43 +55,29 @@ def close(self): self.closed = True def get_images(self): - """ - - """ return [env.render(mode='rgb_array') for env in self.envs] def render(self, mode='human' ): - """ - - """ if self.num_envs == 1: return self.envs[0].render(mode=mode) else: return super().render(mode=mode) def get_env_random_state(self): - """ - - """ return [get_random_state()] def set_env_random_state(self, worker_random_states ): - """ - - """ set_random_state(worker_random_states[0]) def get_attr(self, attr_name, indices=None ): - """Return attribute from vectorized environment (see base class). - - """ + '''Return attribute from vectorized environment (see base class).''' target_envs = self._get_target_envs(indices) return [getattr(env_i, attr_name) for env_i in target_envs] @@ -114,9 +86,7 @@ def set_attr(self, values, indices=None ): - """Set attribute inside vectorized environments (see base class). - - """ + '''Set attribute inside vectorized environments (see base class).''' target_envs = self._get_target_envs(indices) assert len(target_envs) == len(values) for env_i, val_i in zip(target_envs, values): @@ -127,9 +97,7 @@ def env_method(self, method_args=None, method_kwargs=None, indices=None): - """Call instance methods of vectorized environments. - - """ + '''Call instance methods of vectorized environments.''' target_envs = self._get_target_envs(indices) if method_args is None: method_args = [[]] * len(target_envs) @@ -145,10 +113,7 @@ def env_method(self, def _get_target_envs(self, indices ): - """ - - """ assert indices is None or sorted( - indices) == indices, "Indices must be ordered" + indices) == indices, 'Indices must be ordered' indices = self._get_indices(indices) return [self.envs[i] for i in indices] diff --git a/safe_control_gym/envs/env_wrappers/vectorized_env/subproc_vec_env.py b/safe_control_gym/envs/env_wrappers/vectorized_env/subproc_vec_env.py index ba9f1d755..787b1ad18 100644 --- a/safe_control_gym/envs/env_wrappers/vectorized_env/subproc_vec_env.py +++ b/safe_control_gym/envs/env_wrappers/vectorized_env/subproc_vec_env.py @@ -1,30 +1,31 @@ -"""Subprocess vectorized environments. +'''Subprocess vectorized environments. See also: * https://github.com/openai/baselines/blob/master/baselines/common/vec_env/subproc_vec_env.py * https://github.com/DLR-RM/stable-baselines3/blob/master/stable_baselines3/common/vec_env/subproc_vec_env.py +''' -""" import copy -import numpy as np import multiprocessing as mp -from safe_control_gym.utils.utils import get_random_state, set_random_state +import numpy as np + from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env import VecEnv -from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_list, _flatten_obs, CloudpickleWrapper, clear_mpi_env_vars +from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import (CloudpickleWrapper, + _flatten_list, _flatten_obs, + clear_mpi_env_vars) +from safe_control_gym.utils.utils import get_random_state, set_random_state class SubprocVecEnv(VecEnv): - """Multiprocess envs. - - """ + '''Multiprocess envs.''' - def __init__(self, env_fns, spaces=None, context="spawn", n_workers=1): + def __init__(self, env_fns, spaces=None, context='spawn', n_workers=1): self.waiting = False self.closed = False nenvs = len(env_fns) self.n_workers = n_workers - assert nenvs % n_workers == 0, "Number of envs must be divisible by number of workers to run in series" + assert nenvs % n_workers == 0, 'Number of envs must be divisible by number of workers to run in series' env_fns = np.array_split(env_fns, self.n_workers) # Context is necessary for multiprocessing with CUDA, see pytorch.org/docs/stable/notes/multiprocessing.html ctx = mp.get_context(context) @@ -60,7 +61,7 @@ def step_wait(self): results = _flatten_list(results) self.waiting = False obs, rews, dones, infos = zip(*results) - return _flatten_obs(obs), np.stack(rews), np.stack(dones), {"n": infos} + return _flatten_obs(obs), np.stack(rews), np.stack(dones), {'n': infos} def reset(self): self._assert_not_closed() @@ -69,12 +70,10 @@ def reset(self): results = [remote.recv() for remote in self.remotes] results = _flatten_list(results) obs, infos = zip(*results) - return _flatten_obs(obs), {"n": infos} + return _flatten_obs(obs), {'n': infos} def get_images(self): - """Called by parent `render` to support tiling images. - - """ + '''Called by parent `render` to support tiling images.''' self._assert_not_closed() for pipe in self.remotes: pipe.send(('render', None)) @@ -97,7 +96,7 @@ def close(self): self.closed = True def _assert_not_closed(self): - assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()" + assert not self.closed, 'Trying to operate on a SubprocVecEnv after calling close()' def get_env_random_state(self): for remote in self.remotes: @@ -108,21 +107,16 @@ def get_env_random_state(self): def set_env_random_state(self, worker_random_states): for remote, random_state in zip(self.remotes, worker_random_states): remote.send(('set_random_state', random_state)) - res = [remote.recv() for remote in self.remotes] def get_attr(self, attr_name, indices=None): - """Return attribute from vectorized environment (see base class). - - """ + '''Return attribute from vectorized environment (see base class).''' target_remotes, remote_env_indices = self._get_target_envs(indices) for remote, env_indices in zip(target_remotes, remote_env_indices): - remote.send(("get_attr", (env_indices, attr_name))) + remote.send(('get_attr', (env_indices, attr_name))) return _flatten_list([remote.recv() for remote in target_remotes]) def set_attr(self, attr_name, values, indices=None): - """Set attribute inside vectorized environments (see base class). - - """ + '''Set attribute inside vectorized environments (see base class).''' target_remotes, remote_env_indices, splits = self._get_target_envs( indices) value_splits = [] @@ -133,7 +127,7 @@ def set_attr(self, attr_name, values, indices=None): for remote, env_indices, value_split in zip(target_remotes, remote_env_indices, value_splits): - remote.send(("set_attr", (env_indices, attr_name, value_split))) + remote.send(('set_attr', (env_indices, attr_name, value_split))) for remote in target_remotes: remote.recv() @@ -142,9 +136,7 @@ def env_method(self, method_args=None, method_kwargs=None, indices=None): - """Call instance methods of vectorized environments. - - """ + '''Call instance methods of vectorized environments.''' target_remotes, remote_env_indices, splits = self._get_target_envs(indices) method_arg_splits, method_kwarg_splits = [], [] for i in range(len(splits) - 1): @@ -161,13 +153,12 @@ def env_method(self, for remote, env_indices, method_arg_split, method_kwarg_split in zip( target_remotes, remote_env_indices, method_arg_splits, method_kwarg_splits): - remote.send(("env_method", (env_indices, method_name, + remote.send(('env_method', (env_indices, method_name, method_arg_split, method_kwarg_split))) return _flatten_list([remote.recv() for remote in target_remotes]) def _get_target_envs(self, indices): - """ - + ''' Example: n_workers: 3 current envs: [0,1,2,3,4,5] @@ -177,10 +168,10 @@ def _get_target_envs(self, indices): remote_indices: [0,0,1,1] -> [0,1] splits: [0,2] -> [0,2,4] remote_env_indices: [1,1,0,1] -> [1,1], [0,1] + ''' - """ assert indices is None or sorted( - indices) == indices, "Indices must be ordered" + indices) == indices, 'Indices must be ordered' indices = self._get_indices(indices) remote_indices = [idx // self.n_workers for idx in indices] remote_env_indices = [idx % self.n_workers for idx in indices] @@ -193,17 +184,15 @@ def _get_target_envs(self, indices): def worker(remote, parent_remote, env_fn_wrappers): - """Worker func to execute vec_env commands. - - """ + '''Worker func to execute vec_env commands.''' def step_env(env, action): ob, reward, done, info = env.step(action) if done: end_obs = copy.deepcopy(ob) end_info = copy.deepcopy(info) ob, info = env.reset() - info["terminal_observation"] = end_obs - info["terminal_info"] = end_info + info['terminal_observation'] = end_obs + info['terminal_info'] = end_info return ob, reward, done, info parent_remote.close() envs = [env_fn_wrapper() for env_fn_wrapper in env_fn_wrappers.x] @@ -225,24 +214,24 @@ def step_env(env, action): remote.send( CloudpickleWrapper( (envs[0].observation_space, envs[0].action_space))) - elif cmd == "get_random_state": + elif cmd == 'get_random_state': remote.send(CloudpickleWrapper(get_random_state())) - elif cmd == "set_random_state": + elif cmd == 'set_random_state': set_random_state(data) # Placeholder for the return. remote.send(True) - elif cmd == "get_attr": + elif cmd == 'get_attr': env_indices, attr_name = data target_envs = [envs[idx] for idx in env_indices] remote.send([getattr(env, attr_name) for env in target_envs]) - elif cmd == "set_attr": + elif cmd == 'set_attr': env_indices, attr_name, values = data target_envs = [envs[idx] for idx in env_indices] remote.send([ setattr(env, attr_name, value) for env, value in zip(target_envs, values) ]) - elif cmd == "env_method": + elif cmd == 'env_method': env_indices, name, args_list, kwargs_list = data target_envs = [envs[idx] for idx in env_indices] methods = [getattr(env, name) for env in target_envs] diff --git a/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env.py b/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env.py index 449ea0658..db78159e8 100644 --- a/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env.py +++ b/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env.py @@ -1,12 +1,9 @@ -"""Adapted from OpenAI Baselines. +'''Adapted from OpenAI Baselines. See also: * https://github.com/openai/baselines/blob/master/baselines/common/vec_env/vec_env.py * https://github.com/DLR-RM/stable-baselines3/blob/master/stable_baselines3/common/vec_env/base_vec_env.py - -""" -import os -import numpy as np +''' from abc import ABC, abstractmethod @@ -14,12 +11,11 @@ class VecEnv(ABC): - """An abstract asynchronous, vectorized environment. - + '''An abstract asynchronous, vectorized environment. + Used to batch data from multiple copies of an environment, so that each observation becomes a batch of observations, and expected action is a batch of actions to be applied per-environment. - - """ + ''' closed = False viewer = None metadata = {'render.modes': ['human', 'rgb_array']} @@ -35,43 +31,38 @@ def __init__(self, @abstractmethod def reset(self): - """Reset all the environments and return an array of observations, or a dict of observation arrays. + '''Reset all the environments and return an array of observations, or a dict of observation arrays. If step_async is still doing work, that work will be cancelled and step_wait() should not be called until step_async() is invoked again. - - """ + ''' pass @abstractmethod def step_async(self, actions ): - """Tell all the environments to start taking a step with the given actions. + '''Tell all the environments to start taking a step with the given actions. Call step_wait() to get the results of the step. You should not call this if a step_async run is already pending. - - """ + ''' pass @abstractmethod def step_wait(self): - """Wait for the step taken with step_async(). + '''Wait for the step taken with step_async(). Returns (obs, rews, dones, infos): - obs: an array of observations, or a dict of arrays of observations. - rews: an array of rewards - - dones: an array of "episode done" booleans + - dones: an array of 'episode done' booleans - infos: a sequence of info objects - - """ + ''' pass def close_extras(self): - """Clean up the extra resources. Only runs when not self.closed. - - """ + '''Clean up the extra resources. Only runs when not self.closed.''' pass def close(self): @@ -85,18 +76,14 @@ def close(self): def step(self, actions ): - """Step the environments synchronously. - - """ + '''Step the environments synchronously.''' self.step_async(actions) return self.step_wait() def render(self, mode='human' ): - """Display environment via a viewer. - - """ + '''Display environment via a viewer.''' imgs = self.get_images() bigimg = tile_images(imgs) if mode == 'human': @@ -108,9 +95,7 @@ def render(self, raise NotImplementedError def get_images(self): - """Return RGB images from each environment. - - """ + '''Return RGB images from each environment.''' raise NotImplementedError @property @@ -122,22 +107,18 @@ def unwrapped(self): def get_viewer(self): if self.viewer is None: - from gym.envs.classic_control import rendering + from gymnasium.envs.classic_control import rendering self.viewer = rendering.SimpleImageViewer() return self.viewer @abstractmethod def get_attr(self, attr_name, indices=None): - """Return attribute from vectorized environment. - - """ + '''Return attribute from vectorized environment.''' pass @abstractmethod def set_attr(self, attr_name, values, indices=None): - """Set attribute inside vectorized environments. - - """ + '''Set attribute inside vectorized environments.''' pass @abstractmethod @@ -146,15 +127,13 @@ def env_method(self, method_args=None, method_kwargs=None, indices=None): - """Call instance methods of vectorized environments. - - """ + '''Call instance methods of vectorized environments.''' raise NotImplementedError() def _get_indices(self, indices ): - """Convert a flexibly-typed reference to environment indices to an implied list of indices.""" + '''Convert a flexibly-typed reference to environment indices to an implied list of indices.''' if indices is None: indices = range(self.num_envs) elif isinstance(indices, int): @@ -163,7 +142,7 @@ def _get_indices(self, class VecEnvWrapper(VecEnv): - """An environment wrapper that applies to an entire batch of environments at once.""" + '''An environment wrapper that applies to an entire batch of environments at once.''' def __init__(self, venv, @@ -200,7 +179,7 @@ def __getattr__(self, ): if name.startswith('_'): raise AttributeError( - "attempted to get missing private attribute '{}'".format(name)) + f'attempted to get missing private attribute \'{name}\'') return getattr(self.venv, name) def get_attr(self, diff --git a/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env_utils.py b/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env_utils.py index a59a663b1..7bac037c3 100644 --- a/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env_utils.py +++ b/safe_control_gym/envs/env_wrappers/vectorized_env/vec_env_utils.py @@ -1,12 +1,11 @@ -import os import contextlib +import os + import numpy as np class CloudpickleWrapper(object): - """Uses cloudpickle to serialize contents and stop multiprocessing from using pickle. - - """ + '''Uses cloudpickle to serialize contents and stop multiprocessing from using pickle.''' def __init__(self, x): self.x = x @@ -22,15 +21,14 @@ def __setstate__(self, ob): @contextlib.contextmanager def clear_mpi_env_vars(): - """From mpi4py import MPI will call MPI_Init by default. - + '''From mpi4py import MPI will call MPI_Init by default. + If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang. - + This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing Processes. - - """ + ''' removed_environment = {} for k, v in list(os.environ.items()): for prefix in ['OMPI_', 'PMI_']: @@ -44,18 +42,17 @@ def clear_mpi_env_vars(): def tile_images(img_nhwc): - """Tile N images into one big PxQ image. + '''Tile N images into one big PxQ image. (P,Q) are chosen to be as close as possible, and if N is square, then P=Q. - Args: + Args: img_nhwc: list or array of images, ndim=4 once turned into array, n = batch index, h = height, w = width, c = channel. Returns: img_Hh_Ww_c: ndarray with ndim=3. - - """ + ''' img_nhwc = np.asarray(img_nhwc) N, h, w, c = img_nhwc.shape H = int(np.ceil(np.sqrt(N))) @@ -69,9 +66,6 @@ def tile_images(img_nhwc): def _flatten_obs(obs): - """ - - """ assert isinstance(obs, (list, tuple)) assert len(obs) > 0 @@ -83,10 +77,8 @@ def _flatten_obs(obs): def _unflatten_obs(obs): - """ - - """ assert isinstance(obs, (np.ndarray, dict)) + def split_batch(data): return [d[0] for d in np.split(data, len(data))] if isinstance(obs, dict): @@ -99,11 +91,8 @@ def split_batch(data): return split_batch(obs) -def _flatten_list(l): - """ - - """ - assert isinstance(l, (list, tuple)) - assert len(l) > 0 - assert all([len(l_) > 0 for l_ in l]) - return [l__ for l_ in l for l__ in l_] +def _flatten_list(list_to_flatten): + assert isinstance(list_to_flatten, (list, tuple)) + assert len(list_to_flatten) > 0 + assert all([len(l_) > 0 for l_ in list_to_flatten]) + return [l__ for l_ in list_to_flatten for l__ in l_] diff --git a/safe_control_gym/envs/gym_control/assets/cartpole_template.urdf b/safe_control_gym/envs/gym_control/assets/cartpole_template.urdf new file mode 100644 index 000000000..bcfaac5be --- /dev/null +++ b/safe_control_gym/envs/gym_control/assets/cartpole_template.urdf @@ -0,0 +1,78 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/safe_control_gym/envs/gym_control/cartpole.py b/safe_control_gym/envs/gym_control/cartpole.py new file mode 100644 index 000000000..09257fc6b --- /dev/null +++ b/safe_control_gym/envs/gym_control/cartpole.py @@ -0,0 +1,753 @@ +'''Cartpole environment using PyBullet physics. + +Classic cart-pole system implemented by Rich Sutton et al. + * http://incompleteideas.net/sutton/book/code/pole.c + +Also see: + * github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py + * github.com/bulletphysics/bullet3/blob/master/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py +''' + +import math +import os +import xml.etree.ElementTree as etxml +from copy import deepcopy + +import casadi as cs +import numpy as np +import pybullet as p +import pybullet_data +from gymnasium import spaces + +from safe_control_gym.envs.benchmark_env import BenchmarkEnv, Cost, Task +from safe_control_gym.envs.constraints import GENERAL_CONSTRAINTS, SymmetricStateConstraint +from safe_control_gym.math_and_models.normalization import normalize_angle +from safe_control_gym.math_and_models.symbolic_systems import SymbolicModel + + +class CartPole(BenchmarkEnv): + '''Cartpole environment task. + + Including symbolic model, constraints, randomization, adversarial disturbances, + multiple cost functions, stabilization and trajectory tracking references. + + task_config: + info_in_reset: True + randomized_inertial_prop: True + inertial_prop_randomization_info: + pole_length: + distrib: choice + args: [[1,5,10]] + pole_mass: + distrib: uniform + low: 1 + high: 5 + constraints: + - constraint_form: bounded_constraint + constrained_variable: STATE + active_dims: [2, 3] + lower_bounds: [-0.2, -0.2] + upper_bounds: [0.2, 0.2] + tolerance: [0.05, 0.05, 0.05, 0.05] + done_on_violation: True + disturbances: + observation: + - disturbance_func: white_noise + std: 4.0 + action: + - disturbance_func: white_noise + std: 4.0 + adversary_disturbance: dynamics + adversary_disturbance_scale: 0.01 + ''' + + NAME = 'cartpole' + + URDF_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets', 'cartpole_template.urdf') + + AVAILABLE_CONSTRAINTS = { + 'abs_bound': SymmetricStateConstraint + } + AVAILABLE_CONSTRAINTS.update(deepcopy(GENERAL_CONSTRAINTS)) + + DISTURBANCE_MODES = {'observation': {'dim': 4}, 'action': {'dim': 1}, 'dynamics': {'dim': 2}} + + INERTIAL_PROP_RAND_INFO = { + 'pole_length': { # Nominal: 1 + 'distrib': 'choice', + 'args': [[1, 5, 10]] + }, + 'cart_mass': { # Nominal: 1 + 'distrib': 'uniform', + 'low': 0.5, + 'high': 1.5 + }, + 'pole_mass': { # Nominal: 0.1 + 'distrib': 'uniform', + 'low': 0.05, + 'high': 0.15 + } + } + + INIT_STATE_RAND_INFO = { + 'init_x': { + 'distrib': 'uniform', + 'low': -0.05, + 'high': 0.05 + }, + 'init_x_dot': { + 'distrib': 'uniform', + 'low': -0.05, + 'high': 0.05 + }, + 'init_theta': { + 'distrib': 'uniform', + 'low': -0.05, + 'high': 0.05 + }, + 'init_theta_dot': { + 'distrib': 'uniform', + 'low': -0.05, + 'high': 0.05 + } + } + + TASK_INFO = { + 'stabilization_goal': [0], + 'stabilization_goal_tolerance': 0.05, + 'trajectory_type': 'circle', + 'num_cycles': 1, + 'trajectory_plane': 'zx', + 'trajectory_position_offset': [0, 0], + 'trajectory_scale': 0.2 + } + + def __init__(self, + init_state=None, + inertial_prop=None, + # custom args + obs_goal_horizon=0, + obs_wrap_angle=False, + rew_state_weight=1.0, + rew_act_weight=0.0001, + rew_exponential=True, + done_on_out_of_bound=True, + **kwargs + ): + '''Initialize a cartpole environment. + + Args: + init_state (ndarray/dict, optional): The initial state of the environment. + (x, x_dot, theta, theta_dot). + inertial_prop (dict, optional): The ground truth inertial properties of the environment. + obs_goal_horizon (int): How many future goal states to append to obervation. + obs_wrap_angle (bool): If to wrap angle to [-pi, pi] when used in observation. + rew_state_weight (list/ndarray): Quadratic weights for state in rl reward. + rew_act_weight (list/ndarray): Quadratic weights for action in rl reward. + rew_exponential (bool): If to exponentiate negative quadratic cost to positive, bounded [0,1] reward. + done_on_out_of_bound (bool): If to termiante when state is out of bound. + ''' + self.obs_goal_horizon = obs_goal_horizon + self.obs_wrap_angle = obs_wrap_angle + self.rew_state_weight = np.array(rew_state_weight, ndmin=1, dtype=float) + self.rew_act_weight = np.array(rew_act_weight, ndmin=1, dtype=float) + self.rew_exponential = rew_exponential + self.done_on_out_of_bound = done_on_out_of_bound + # BenchmarkEnv constructor, called after defining the custom args, + # since some BenchmarkEnv init setup can be task(custom args)-dependent. + super().__init__(init_state=init_state, inertial_prop=inertial_prop, **kwargs) + + # Create PyBullet client connection. + self.PYB_CLIENT = -1 + if self.GUI: + self.PYB_CLIENT = p.connect(p.GUI) + else: + self.PYB_CLIENT = p.connect(p.DIRECT) + # disable urdf caching for randomization via reloading urdf + p.setPhysicsEngineParameter(enableFileCaching=0) + + # Set GUI and rendering constants. + self.RENDER_HEIGHT = int(200) + self.RENDER_WIDTH = int(320) + + # Set the initial state. + if init_state is None: + self.INIT_X, self.INIT_X_DOT, self.INIT_THETA, self.INIT_THETA_DOT = np.zeros(4) + elif isinstance(init_state, np.ndarray): + self.INIT_X, self.INIT_X_DOT, self.INIT_THETA, self.INIT_THETA_DOT = init_state + elif isinstance(init_state, dict): + self.INIT_X = init_state.get('init_x', 0) + self.INIT_X_DOT = init_state.get('init_x_dot', 0) + self.INIT_THETA = init_state.get('init_theta', 0) + self.INIT_THETA_DOT = init_state.get('init_theta_dot', 0) + else: + raise ValueError('[ERROR] in CartPole.__init__(), init_state incorrect format.') + + # Get physical properties from URDF (as default parameters). + self.GRAVITY_ACC = 9.8 + EFFECTIVE_POLE_LENGTH, POLE_MASS, CART_MASS = self._parse_urdf_parameters(self.URDF_PATH) + + # Store ground truth parameters. + if inertial_prop is None: + self.EFFECTIVE_POLE_LENGTH = EFFECTIVE_POLE_LENGTH + self.POLE_MASS = POLE_MASS + self.CART_MASS = CART_MASS + elif isinstance(inertial_prop, dict): + self.EFFECTIVE_POLE_LENGTH = inertial_prop.get('pole_length', EFFECTIVE_POLE_LENGTH) + self.POLE_MASS = inertial_prop.get('pole_mass', POLE_MASS) + self.CART_MASS = inertial_prop.get('cart_mass', CART_MASS) + else: + raise ValueError('[ERROR] in CartPole.__init__(), inertial_prop incorrect format.') + + # Create X_GOAL and U_GOAL references for the assigned task. + self.U_GOAL = np.zeros(1) + if self.TASK == Task.STABILIZATION: + self.X_GOAL = np.hstack([self.TASK_INFO['stabilization_goal'][0], 0., 0., 0.]) # x = {x, x_dot, theta, theta_dot}. + elif self.TASK == Task.TRAJ_TRACKING: + POS_REF, VEL_REF, _ = self._generate_trajectory(traj_type=self.TASK_INFO['trajectory_type'], + traj_length=self.EPISODE_LEN_SEC, + num_cycles=self.TASK_INFO['num_cycles'], + traj_plane=self.TASK_INFO['trajectory_plane'], + position_offset=np.array(self.TASK_INFO['trajectory_position_offset']), + scaling=self.TASK_INFO['trajectory_scale'], + sample_time=self.CTRL_TIMESTEP + ) + self.X_GOAL = np.vstack([ + POS_REF[:, 0], # Possible feature: add initial position. + VEL_REF[:, 0], + np.zeros(POS_REF.shape[0]), + np.zeros(VEL_REF.shape[0]) + ]).transpose() + + # Set prior/symbolic info. + self._setup_symbolic() + + def step(self, action): + '''Advances the environment by one control step. + + Args: + action (ndarray): The action applied to the environment for the step. + + Returns: + obs (ndarray): The state of the environment after the step. + reward (float): The scalar reward/cost of the step. + done (bool): Whether the conditions for the end of an episode are met in the step. + info (dict): A dictionary with information about the constraints evaluations and violations. + ''' + + force = super().before_step(action) + # Advance the simulation. + self._advance_simulation(force) + # Update the state. + self.state = np.hstack( + (p.getJointState(self.CARTPOLE_ID, jointIndex=0, + physicsClientId=self.PYB_CLIENT)[0:2], p.getJointState(self.CARTPOLE_ID, jointIndex=1, physicsClientId=self.PYB_CLIENT)[0:2])) + # Standard Gym return. + obs = self._get_observation() + rew = self._get_reward() + done = self._get_done() + info = self._get_info() + obs, rew, done, info = super().after_step(obs, rew, done, info) + return obs, rew, done, info + + def reset(self, seed=None): + '''(Re-)initializes the environment to start an episode. + + Mandatory to call at least once after __init__(). + + Args: + seed (int): An optional seed to reseed the environment. + + Returns: + obs (ndarray): The initial state of the environment. + info (dict): A dictionary with information about the dynamics and constraints symbolic models. + ''' + + super().before_reset(seed=seed) + # PyBullet simulation reset. + p.resetSimulation(physicsClientId=self.PYB_CLIENT) + p.setGravity(0, 0, -self.GRAVITY_ACC, physicsClientId=self.PYB_CLIENT) + p.setTimeStep(self.PYB_TIMESTEP, physicsClientId=self.PYB_CLIENT) + p.setRealTimeSimulation(0, physicsClientId=self.PYB_CLIENT) + p.setAdditionalSearchPath(pybullet_data.getDataPath(), physicsClientId=self.PYB_CLIENT) + # Choose randomized or deterministic inertial properties. + prop_values = {'pole_length': self.EFFECTIVE_POLE_LENGTH, 'cart_mass': self.CART_MASS, 'pole_mass': self.POLE_MASS} + if self.RANDOMIZED_INERTIAL_PROP: + prop_values = self._randomize_values_by_info(prop_values, self.INERTIAL_PROP_RAND_INFO) + if any(phy_quantity < 0 for phy_quantity in prop_values.values()): + raise ValueError('[ERROR] in CartPole.reset(), negative randomized inertial properties.') + self.OVERRIDDEN_EFFECTIVE_POLE_LENGTH = prop_values['pole_length'] + self.OVERRIDDEN_CART_MASS = prop_values['cart_mass'] + self.OVERRIDDEN_POLE_MASS = prop_values['pole_mass'] + # See `slender rod`, https://en.wikipedia.org/wiki/List_of_moments_of_inertia. + OVERRIDDEN_POLE_INERTIA = (1 / 12) * self.OVERRIDDEN_POLE_MASS * (2 * self.OVERRIDDEN_EFFECTIVE_POLE_LENGTH)**2 + # Load the cartpole with new urdf. + override_urdf_tree = self._create_urdf(self.URDF_PATH, length=self.OVERRIDDEN_EFFECTIVE_POLE_LENGTH, inertia=OVERRIDDEN_POLE_INERTIA) + self.override_path = os.path.join(self.output_dir, f'pid-{os.getpid()}_id-{self.idx}_cartpole.urdf') + override_urdf_tree.write(self.override_path) + self.CARTPOLE_ID = p.loadURDF( + self.override_path, + basePosition=[0, 0, 0], + physicsClientId=self.PYB_CLIENT) + # Remove cache file after loading it into PyBullet. + os.remove(self.override_path) + # Cartpole settings. + for i in [-1, 0, 1]: # Slider, cart, and pole. + p.changeDynamics(self.CARTPOLE_ID, linkIndex=i, linearDamping=0, angularDamping=0, physicsClientId=self.PYB_CLIENT) + for i in [0, 1]: # Slider-to-cart and cart-to-pole joints. + p.setJointMotorControl2(self.CARTPOLE_ID, jointIndex=i, controlMode=p.VELOCITY_CONTROL, force=0, physicsClientId=self.PYB_CLIENT) + # Override inertial properties. + p.changeDynamics( + self.CARTPOLE_ID, + linkIndex=0, # Cart. + mass=self.OVERRIDDEN_CART_MASS, + physicsClientId=self.PYB_CLIENT) + p.changeDynamics( + self.CARTPOLE_ID, + linkIndex=1, # Pole. + mass=self.OVERRIDDEN_POLE_MASS, + physicsClientId=self.PYB_CLIENT) + # Randomize initial state. + init_values = {'init_x': self.INIT_X, 'init_x_dot': self.INIT_X_DOT, 'init_theta': self.INIT_THETA, 'init_theta_dot': self.INIT_THETA_DOT} + if self.RANDOMIZED_INIT: + init_values = self._randomize_values_by_info(init_values, self.INIT_STATE_RAND_INFO) + OVERRIDDEN_INIT_X = init_values['init_x'] + OVERRIDDEN_INIT_X_DOT = init_values['init_x_dot'] + OVERRIDDEN_INIT_THETA = init_values['init_theta'] + OVERRIDDEN_INIT_THETA_DOT = init_values['init_theta_dot'] + p.resetJointState( + self.CARTPOLE_ID, + jointIndex=0, # Slider-to-cart joint. + targetValue=OVERRIDDEN_INIT_X, + targetVelocity=OVERRIDDEN_INIT_X_DOT, + physicsClientId=self.PYB_CLIENT) + p.resetJointState( + self.CARTPOLE_ID, + jointIndex=1, # Cart-to-pole joints. + targetValue=OVERRIDDEN_INIT_THETA, + targetVelocity=OVERRIDDEN_INIT_THETA_DOT, + physicsClientId=self.PYB_CLIENT) + # Compute state (x, x_dot, theta, theta_dot). + self.state = np.hstack( + (p.getJointState(self.CARTPOLE_ID, jointIndex=0, + physicsClientId=self.PYB_CLIENT)[0:2], p.getJointState(self.CARTPOLE_ID, jointIndex=1, physicsClientId=self.PYB_CLIENT)[0:2])) + # Debug visualization if GUI enabled + self.line = None + obs, info = self._get_observation(), self._get_reset_info() + obs, info = super().after_reset(obs, info) + # Return either an observation and dictionary or just the observation. + if self.INFO_IN_RESET: + return obs, info + else: + return obs + + def render(self, mode='human'): + '''Retrieves a frame from PyBullet rendering. + + Args: + mode (str): Unused. + + Returns: + frame (ndarray): A multidimensional array with the RGB frame captured by PyBullet's camera. + ''' + if self.PYB_CLIENT >= 0: + VIEW_MATRIX = p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=[0, 0, 0], + distance=2, + yaw=0, + pitch=0.3, + roll=0, + upAxisIndex=2, + physicsClientId=self.PYB_CLIENT) + PROJ_MATRIX = p.computeProjectionMatrixFOV(fov=60, + aspect=float(self.RENDER_WIDTH) / self.RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0, + physicsClientId=self.PYB_CLIENT) + (w, h, rgb, _, _) = p.getCameraImage(width=self.RENDER_WIDTH, + height=self.RENDER_HEIGHT, + renderer=p.ER_BULLET_HARDWARE_OPENGL, + viewMatrix=VIEW_MATRIX, + projectionMatrix=PROJ_MATRIX, + physicsClientId=self.PYB_CLIENT) + return np.reshape(rgb, (h, w, 4)) + + def close(self): + '''Clean up the environment and PyBullet connection.''' + if self.PYB_CLIENT >= 0: + p.disconnect(physicsClientId=self.PYB_CLIENT) + self.PYB_CLIENT = -1 + + def _setup_symbolic(self, prior_prop={}, **kwargs): + '''Creates symbolic (CasADi) models for dynamics, observation, and cost. + + Args: + prior_prop (dict): specify the prior inertial prop to use in the symbolic model. + ''' + length = prior_prop.get('pole_length', self.EFFECTIVE_POLE_LENGTH) + m = prior_prop.get('pole_mass', self.POLE_MASS) + M = prior_prop.get('cart_mass', self.CART_MASS) + Mm, ml = m + M, m * length + g = self.GRAVITY_ACC + dt = self.CTRL_TIMESTEP + # Input variables. + x = cs.MX.sym('x') + x_dot = cs.MX.sym('x_dot') + theta = cs.MX.sym('theta') + theta_dot = cs.MX.sym('theta_dot') + X = cs.vertcat(x, x_dot, theta, theta_dot) + U = cs.MX.sym('U') + nx = 4 + nu = 1 + # Dynamics. + temp_factor = (U + ml * theta_dot**2 * cs.sin(theta)) / Mm + theta_dot_dot = ((g * cs.sin(theta) - cs.cos(theta) * temp_factor) / (length * (4.0 / 3.0 - m * cs.cos(theta)**2 / Mm))) + X_dot = cs.vertcat(x_dot, temp_factor - ml * theta_dot_dot * cs.cos(theta) / Mm, theta_dot, theta_dot_dot) + # Observation. + Y = cs.vertcat(x, x_dot, theta, theta_dot) + # Define cost (quadratic form). + Q = cs.MX.sym('Q', nx, nx) + R = cs.MX.sym('R', nu, nu) + Xr = cs.MX.sym('Xr', nx, 1) + Ur = cs.MX.sym('Ur', nu, 1) + cost_func = 0.5 * (X - Xr).T @ Q @ (X - Xr) + 0.5 * (U - Ur).T @ R @ (U - Ur) + # Define dynamics and cost dictionaries. + dynamics = {'dyn_eqn': X_dot, 'obs_eqn': Y, 'vars': {'X': X, 'U': U}} + cost = {'cost_func': cost_func, 'vars': {'X': X, 'U': U, 'Xr': Xr, 'Ur': Ur, 'Q': Q, 'R': R}} + # Additional params to cache + params = { + # prior inertial properties + 'pole_length': length, + 'pole_mass': m, + 'cart_mass': M, + # equilibrium point for linearization + 'X_EQ': np.zeros(self.state_dim), + 'U_EQ': np.atleast_2d(self.U_GOAL)[0, :], + } + # Setup symbolic model. + self.symbolic = SymbolicModel(dynamics=dynamics, cost=cost, dt=dt, params=params) + + def _set_action_space(self): + '''Sets the action space of the environment.''' + self.action_scale = 10 + self.physical_action_bounds = (-1 * np.atleast_1d(self.action_scale), np.atleast_1d(self.action_scale)) + self.action_threshold = 1 if self.NORMALIZED_RL_ACTION_SPACE else self.action_scale + self.action_space = spaces.Box(low=-self.action_threshold, high=self.action_threshold, shape=(1,)) + # Define action/input labels and units. + self.ACTION_LABELS = ['U'] + self.ACTION_UNITS = ['N'] if not self.NORMALIZED_RL_ACTION_SPACE else ['-'] + + def _set_observation_space(self): + '''Sets the observation space of the environment.''' + # Angle at which to fail the episode. + self.theta_threshold_radians = 90 * math.pi / 180 + # NOTE: different value in PyBullet gym (0.4) and OpenAI gym (2.4). + self.x_threshold = 2.4 + # Limit set to 2x: i.e. a failing observation is still within bounds. + obs_bound = np.array([self.x_threshold * 2, np.finfo(np.float32).max, self.theta_threshold_radians * 2, np.finfo(np.float32).max]) + self.state_space = spaces.Box(low=-obs_bound, high=obs_bound, dtype=np.float32) + + # Concatenate goal info for RL + if self.COST == Cost.RL_REWARD and self.TASK == Task.TRAJ_TRACKING and self.obs_goal_horizon > 0: + # include future goal state(s) + # e.g. horizon=1, obs = {state, state_target} + mul = 1 + self.obs_goal_horizon + obs_bound = np.concatenate([obs_bound] * mul) + elif self.COST == Cost.RL_REWARD and self.TASK == Task.STABILIZATION and self.obs_goal_horizon > 0: + obs_bound = np.concatenate([obs_bound] * 2) + # Define obs space exposed to the controller + # Note obs space is often different to state space for RL (with additional task info) + self.observation_space = spaces.Box(low=-obs_bound, high=obs_bound, dtype=np.float32) + + # Define obs/state labels and units. + self.STATE_LABELS = ['x', 'x_dot', 'theta', 'theta_dot'] + self.STATE_UNITS = ['m', 'm/s', 'rad', 'rad/s'] + + def _preprocess_control(self, action): + '''Converts the raw action input into the one used by .step(). + + Args: + action (ndarray): The raw action input. + + Returns: + force (float): The scalar, clipped force to apply to the cart. + ''' + action = self.denormalize_action(action) + self.current_physical_action = action + + # Apply disturbances. + if 'action' in self.disturbances: + action = self.disturbances['action'].apply(action, self) + if self.adversary_disturbance == 'action' and self.adv_action is not None: + action = action + self.adv_action + self.current_noisy_physical_action = action + + # Save the actual input. + force = np.clip(action, self.physical_action_bounds[0], self.physical_action_bounds[1]) + self.current_clipped_action = force + + return force[0] # Only use the scalar value. + + def normalize_action(self, action): + '''Converts a physical action into an normalized action if necessary. + + Args: + action (ndarray): The action to be converted. + + Returns: + normalized_action (ndarray): The action in the correct action space. + ''' + if self.NORMALIZED_RL_ACTION_SPACE: + action = action / self.action_scale + + return action + + def denormalize_action(self, action): + '''Converts a normalized action into a physical action if necessary. + + Args: + action (ndarray): The action to be converted. + + Returns: + physical_action (ndarray): The physical action. + ''' + if self.NORMALIZED_RL_ACTION_SPACE: + action = self.action_scale * action + + return action + + def _advance_simulation(self, force): + '''Apply the commanded forces and adversarial actions to the cartpole. + + The PyBullet simulation is stepped PYB_FREQ/CTRL_FREQ times. + + Args: + force (float): The force to apply to the slider-to-cart joint. + ''' + tab_force = None + # Determine the disturbance force. + passive_disturb = 'dynamics' in self.disturbances + adv_disturb = self.adversary_disturbance == 'dynamics' + if passive_disturb or adv_disturb: + tab_force = np.zeros(2) + if passive_disturb: + tab_force = self.disturbances['dynamics'].apply(tab_force, self) + if adv_disturb and self.adv_action is not None: + tab_force = tab_force + self.adv_action + # Clear adversary's action, wait for the next one. + self.adv_action = None + for _ in range(self.PYB_STEPS_PER_CTRL): + # apply disturbance (by tabbing pole on x-z plane). + if tab_force is not None: + # Convert 2D force to 3D on for PyBullet. + tab_force_3d = [float(tab_force[0]), 0.0, float(tab_force[1])] + p.applyExternalForce( + self.CARTPOLE_ID, + linkIndex=1, # Pole link. + forceObj=tab_force_3d, + posObj=p.getLinkState( + self.CARTPOLE_ID, + linkIndex=1, # Pole link. + physicsClientId=self.PYB_CLIENT)[0], # exert force on pole center + flags=p.WORLD_FRAME, + physicsClientId=self.PYB_CLIENT) + # Debug visualization + if self.GUI: + center = np.asarray(p.getLinkState(self.CARTPOLE_ID, linkIndex=1, physicsClientId=self.PYB_CLIENT)[0]) + ff = np.asarray(tab_force_3d) * 10 + if self.line is None: + self.line = p.addUserDebugLine(center.tolist(), (center - ff).tolist(), lineColorRGB=[0, 0, 0], lineWidth=1) + else: + p.addUserDebugLine(center.tolist(), (center - ff).tolist(), lineColorRGB=[0, 0, 0], lineWidth=1, replaceItemUniqueId=self.line) + # Apply control. + p.setJointMotorControl2( + self.CARTPOLE_ID, + jointIndex=0, # Slider-to-cart joint. + controlMode=p.TORQUE_CONTROL, + force=force, + physicsClientId=self.PYB_CLIENT) + # Step simulation and counter. + p.stepSimulation(physicsClientId=self.PYB_CLIENT) + + def _get_observation(self): + '''Returns the current observation (state) of the environment. + + Returns: + obs (ndarray): The state (x, x_dot, theta, theta_dot) of the cartpole. + ''' + if not np.array_equal(self.state, np.clip(self.state, self.state_space.low, self.state_space.high)) and self.VERBOSE: + print('[WARNING]: observation was clipped in CartPole._get_observation().') + # Apply observation disturbance. + obs = deepcopy(self.state) + if 'observation' in self.disturbances: + obs = self.disturbances['observation'].apply(obs, self) + # Wrap angle to constrain state space, useful in swing-up task. + if self.obs_wrap_angle: + obs[2] = normalize_angle(obs[2]) + + # Concatenate goal info (goal state(s)) for RL + # Plus two because ctrl_step_counter has not incremented yet, and we want to return the obs (which would be + # ctrl_step_counter + 1 as the action has already been applied), and the next state (+ 2) for the RL to see + # the next state. + if self.at_reset: + obs = self.extend_obs(obs, 1) + else: + obs = self.extend_obs(obs, self.ctrl_step_counter + 2) + return obs + + def _get_reward(self): + '''Computes the current step's reward value. + + Returns: + reward (float): The evaluated reward/cost. + ''' + if self.COST == Cost.RL_REWARD: + # negative quadratic reward with angle wrapped around + state = deepcopy(self.state) + state[2] = normalize_angle(state[2]) + act = np.asarray(self.current_noisy_physical_action) + if self.TASK == Task.STABILIZATION: + state_error = state - self.X_GOAL + dist = np.sum(self.rew_state_weight * state_error * state_error) + dist += np.sum(self.rew_act_weight * act * act) + if self.TASK == Task.TRAJ_TRACKING: + wp_idx = min(self.ctrl_step_counter + 1, self.X_GOAL.shape[0] - 1) # +1 because state has already advanced but counter not incremented. + state_error = state - self.X_GOAL[wp_idx] + dist = np.sum(self.rew_state_weight * state_error * state_error) + dist += np.sum(self.rew_act_weight * act * act) + rew = -dist + # convert rew to be positive and bounded [0,1] + if self.rew_exponential: + rew = np.exp(rew) + return rew + if self.COST == Cost.QUADRATIC: + if self.TASK == Task.STABILIZATION: + return float( + -1 * self.symbolic.loss(x=self.state, + Xr=self.X_GOAL, + u=self.current_clipped_action, + Ur=self.U_GOAL, + Q=self.Q, + R=self.R)['l']) + if self.TASK == Task.TRAJ_TRACKING: + return float( + -1 * self.symbolic.loss(x=self.state, + Xr=self.X_GOAL[self.ctrl_step_counter, :], + u=self.current_clipped_action, + Ur=self.U_GOAL, + Q=self.Q, + R=self.R)['l']) + + def _get_done(self): + '''Computes the conditions for termination of an episode. + + Returns: + done (bool): Whether an episode is over. + ''' + # Done if goal reached for stabilization task with quadratic cost. + if self.TASK == Task.STABILIZATION: + self.goal_reached = bool(np.linalg.norm(self.state - self.X_GOAL) < self.TASK_INFO['stabilization_goal_tolerance']) + if self.goal_reached: + return True + # Done if state is out-of-bounds. + if self.done_on_out_of_bound: + x, _, theta, _ = self.state + if x < -self.x_threshold or x > self.x_threshold or theta < -self.theta_threshold_radians or theta > self.theta_threshold_radians: + self.out_of_bounds = True + return True + self.out_of_bounds = False + return False + + def _get_info(self): + '''Generates the info dictionary returned by every call to .step(). + + Returns: + info (dict): A dictionary with information about the constraints evaluations and violations. + ''' + info = {} + if self.TASK == Task.STABILIZATION and self.COST == Cost.QUADRATIC: + info['goal_reached'] = self.goal_reached # Add boolean flag for the goal being reached. + if self.done_on_out_of_bound: + info['out_of_bounds'] = self.out_of_bounds + # Add MSE. + state = deepcopy(self.state) + info['mse'] = np.sum(state ** 2) + return info + + def _get_reset_info(self): + '''Generates the info dictionary returned by every call to .reset(). + + Returns: + info (dict): A dictionary with information about the dynamics and constraints symbolic models. + ''' + info = {} + info['symbolic_model'] = self.symbolic + info['physical_parameters'] = { + 'pole_effective_length': self.OVERRIDDEN_EFFECTIVE_POLE_LENGTH, + 'pole_mass': self.OVERRIDDEN_POLE_MASS, + 'cart_mass': self.OVERRIDDEN_CART_MASS + } + info['x_reference'] = self.X_GOAL + info['u_reference'] = self.U_GOAL + if self.constraints is not None: + info['symbolic_constraints'] = self.constraints.get_all_symbolic_models() + # NOTE: Cannot evaluate constraints on reset/without inputs. + info['constraint_values'] = self.constraints.get_values(self, only_state=True) # Fix for input constraints only + return info + + def _parse_urdf_parameters(self, file_name): + '''Parses an URDF file for the robot's properties. + + Args: + file_name (str, optional): The .urdf file from which the properties should be pased. + + Returns: + EFFECTIVE_POLE_LENGTH (float): The effective pole length. + POLE_MASS (float): The pole mass. + CART_MASS (float): The cart mass. + ''' + URDF_TREE = (etxml.parse(file_name)).getroot() + EFFECTIVE_POLE_LENGTH = 0.5 * float(URDF_TREE[3][0][0][0].attrib['size'].split(' ')[-1]) # Note: HALF length of pole. + POLE_MASS = float(URDF_TREE[3][1][1].attrib['value']) + CART_MASS = float(URDF_TREE[1][2][0].attrib['value']) + return EFFECTIVE_POLE_LENGTH, POLE_MASS, CART_MASS + + def _create_urdf(self, file_name, length=None, inertia=None): + '''For domain randomization. + + Args: + file_name (str): Path to the base URDF with attributes to modify. + length (float): Overriden effective pole length. + inertia (float): Pole inertia (symmetric, Ixx & Iyy). + + Returns: + tree (obj): xml tree object. + ''' + tree = etxml.parse(file_name) + root = tree.getroot() + # Overwrite pod length. + if length is not None: + # Pole visual geometry box. + out = root[3][0][0][0].attrib['size'] + out = ' '.join(out.split(' ')[:-1] + [str(2 * length)]) + root[3][0][0][0].attrib['size'] = out + # Pole visual origin. + out = root[3][0][1].attrib['xyz'] + out = ' '.join(out.split(' ')[:-1] + [str(length)]) + root[3][0][1].attrib['xyz'] = out + # Pole inertial origin. + out = root[3][1][0].attrib['xyz'] + out = ' '.join(out.split(' ')[:-1] + [str(length)]) + root[3][1][0].attrib['xyz'] = out + # Pole inertia. + root[3][1][2].attrib['ixx'] = str(inertia) + root[3][1][2].attrib['iyy'] = str(inertia) + root[3][1][2].attrib['izz'] = str(0.0) + # Pole collision geometry box. + out = root[3][2][0][0].attrib['size'] + out = ' '.join(out.split(' ')[:-1] + [str(2 * length)]) + root[3][2][0][0].attrib['size'] = out + # Pole collision origin. + out = root[3][2][1].attrib['xyz'] + out = ' '.join(out.split(' ')[:-1] + [str(length)]) + root[3][2][1].attrib['xyz'] = out + return tree diff --git a/safe_control_gym/envs/gym_control/cartpole.yaml b/safe_control_gym/envs/gym_control/cartpole.yaml new file mode 100644 index 000000000..5c5cf4260 --- /dev/null +++ b/safe_control_gym/envs/gym_control/cartpole.yaml @@ -0,0 +1,38 @@ +# Simulation options +info_in_reset: False +ctrl_freq: 50 +pyb_freq: 50 +gui: False +normalized_rl_action_space: False +episode_len_sec: 5 +# State initialization +init_state: null +randomized_init: True +init_state_randomization_info: null +# Randomization +inertial_prop: null +randomized_inertial_prop: False +inertial_prop_randomization_info: null +# Task +task: stabilization +task_info: null +cost: rl_reward +# Disturbances +disturbances: null +adversary_disturbance: null +adversary_disturbance_offset: 0.0 +adversary_disturbance_scale: 0.01 +# Constraints +constraints: null +done_on_violation: False +use_constraint_penalty: False +constraint_penalty: -1 +# Misc +verbose: False +# RL Hyper-parameters +obs_wrap_angle: False +obs_goal_horizon: 0 +rew_state_weight: 1.0 +rew_act_weight: 0.0001 +rew_exponential: True +done_on_out_of_bound: True diff --git a/safe_control_gym/envs/gym_pybullet_drones/assets/cf2.dae b/safe_control_gym/envs/gym_pybullet_drones/assets/cf2.dae index 31caea1b4..f2970bda9 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/assets/cf2.dae +++ b/safe_control_gym/envs/gym_pybullet_drones/assets/cf2.dae @@ -1864,4 +1864,4 @@ - \ No newline at end of file + diff --git a/safe_control_gym/envs/gym_pybullet_drones/assets/cf2x.urdf b/safe_control_gym/envs/gym_pybullet_drones/assets/cf2x.urdf index 4f5058556..fd6d08123 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/assets/cf2x.urdf +++ b/safe_control_gym/envs/gym_pybullet_drones/assets/cf2x.urdf @@ -4,7 +4,7 @@ - + @@ -23,10 +23,10 @@ - + - + @@ -34,7 +34,7 @@ - + @@ -99,4 +99,3 @@ - diff --git a/safe_control_gym/envs/gym_pybullet_drones/assets/gate.obj b/safe_control_gym/envs/gym_pybullet_drones/assets/gate.obj index 1c2f56409..f128b36af 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/assets/gate.obj +++ b/safe_control_gym/envs/gym_pybullet_drones/assets/gate.obj @@ -29633,18405 +29633,18404 @@ vt 0.1563 0.8281 1.0000 g 18370_Shinto_Torii_Gate_v1 s 4 -f 1/1/1 2/2/1 3/3/2 4/4/3 -f 5/5/4 1/1/1 4/4/3 6/6/5 -f 7/7/6 8/8/6 2/2/1 1/1/1 -f 9/9/6 7/7/6 1/1/1 5/5/4 -f 10/10/7 11/11/4 12/12/8 13/13/8 -f 14/14/9 15/15/9 11/11/4 10/10/7 -f 11/11/4 5/5/4 6/6/5 12/12/8 -f 15/15/9 9/9/6 5/5/4 11/11/4 -f 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18388/1509/5625 +f 18391/1503/5624 18390/1502/5624 18388/1509/5625 18389/1508/5625 +f 18194/1481/5176 18392/1507/5176 18383/1490/5165 18192/1449/9713 +f 18197/1474/5178 18393/1506/5178 18392/1507/5176 18194/1481/5176 +f 18392/1507/5176 18389/1508/5625 18385/1486/9713 18383/1490/5165 +f 18393/1506/5178 18391/1503/5624 18389/1508/5625 18392/1507/5176 +f 18394/1504/5623 18203/1468/5180 18071/1469/138 18302/1505/139 +f 18395/1500/5180 18394/1504/5623 18302/1505/139 18298/1501/138 +f 18393/1506/5178 18197/1474/5178 18203/1468/5180 18394/1504/5623 +f 18391/1503/5624 18393/1506/5178 18394/1504/5623 18395/1500/5180 +f 15238/812/5623 18396/1498/5623 18296/1499/139 7319/815/138 +f 15236/816/5178 18390/1502/5624 18396/1498/5623 15238/812/5623 +f 18396/1498/5623 18395/1500/5180 18298/1501/138 18296/1499/139 +f 18390/1502/5624 18391/1503/5624 18395/1500/5180 18396/1498/5623 # 18400 polygons - diff --git a/safe_control_gym/envs/gym_pybullet_drones/assets/gate.urdf b/safe_control_gym/envs/gym_pybullet_drones/assets/gate.urdf index d531445a8..357ebc9f8 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/assets/gate.urdf +++ b/safe_control_gym/envs/gym_pybullet_drones/assets/gate.urdf @@ -2,7 +2,7 @@ - + @@ -14,10 +14,10 @@ - + - + + --> - \ No newline at end of file + diff --git a/safe_control_gym/envs/gym_pybullet_drones/assets/sphere.urdf b/safe_control_gym/envs/gym_pybullet_drones/assets/sphere.urdf index 121064fc7..54d2d983a 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/assets/sphere.urdf +++ b/safe_control_gym/envs/gym_pybullet_drones/assets/sphere.urdf @@ -21,4 +21,4 @@ - \ No newline at end of file + diff --git a/safe_control_gym/envs/gym_pybullet_drones/quadrotor.yaml b/safe_control_gym/envs/gym_pybullet_drones/quadrotor.yaml index b77c7443f..314d8f368 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/quadrotor.yaml +++ b/safe_control_gym/envs/gym_pybullet_drones/quadrotor.yaml @@ -15,21 +15,21 @@ init_state_randomization_info: null inertial_prop: null randomized_inertial_prop: False inertial_prop_randomization_info: null -# Task +# Task task: stabilization task_info: null cost: rl_reward # Disturbances disturbances: null -adversary_disturbance: null +adversary_disturbance: null adversary_disturbance_offset: 0.0 adversary_disturbance_scale: 0.01 # Constraints constraints: null done_on_violation: False use_constraint_penalty: False -constraint_penalty: -1 -# Misc +constraint_penalty: -1 +# Misc verbose: False # RL Hyper-parameters norm_act_scale: 0.1 diff --git a/safe_control_gym/controllers/cbf/__init__.py b/safe_control_gym/experiments/__init__.py similarity index 100% rename from safe_control_gym/controllers/cbf/__init__.py rename to safe_control_gym/experiments/__init__.py diff --git a/safe_control_gym/experiments/base_experiment.py b/safe_control_gym/experiments/base_experiment.py new file mode 100644 index 000000000..14ff4e43b --- /dev/null +++ b/safe_control_gym/experiments/base_experiment.py @@ -0,0 +1,477 @@ +'''To standardize training/evaluation interface.''' + +from collections import defaultdict +from copy import deepcopy +from time import time + +import gymnasium as gym +import numpy as np +from munch import munchify +from termcolor import colored + +from safe_control_gym.math_and_models.metrics.performance_metrics import compute_cvar +from safe_control_gym.utils.utils import is_wrapped + + +class BaseExperiment: + '''Generic Experiment Class.''' + + def __init__(self, + env, + ctrl, + train_env=None, + safety_filter=None, + verbose: bool = False, + ): + '''Creates a generic experiment class to run evaluations and collect standard metrics. + + Args: + env (BenchmarkEnv): The environment for the task. + ctrl (BaseController): The controller for the task. + train_env (BenchmarkEnv): The environment used for training. + safety_filter (BaseSafetyFilter): The safety filter to filter the controller. + verbose (bool, optional): If to suppress BaseExperiment print statetments. + ''' + + self.metric_extractor = MetricExtractor() + self.verbose = verbose + self.env = env + if not is_wrapped(self.env, RecordDataWrapper): + self.env = RecordDataWrapper(self.env) + self.ctrl = ctrl + + self.train_env = train_env + if train_env is not None and not is_wrapped(self.train_env, RecordDataWrapper): + self.train_env = RecordDataWrapper(self.train_env) + self.safety_filter = safety_filter + + self.reset() + + def run_evaluation(self, training=False, n_episodes=None, n_steps=None, log_freq=None, verbose=True, **kwargs): + '''Evaluate a trained controller. + + Args: + training (bool): Whether run_evaluation is being run as part of a training loop or not. + n_episodes (int): Number of runs to execute. + n_steps (int): The number of steps to collect in total. + log_freq (int): The frequency with which to log information. + + Returns: + trajs_data (dict): The raw data from the executed runs. + metrics (dict): The metrics calculated from the raw data. + ''' + + if not training: + self.reset() + trajs_data = self._execute_evaluations(log_freq=log_freq, n_episodes=n_episodes, n_steps=n_steps, **kwargs) + metrics = self.compute_metrics(trajs_data) + + # terminal printouts + if verbose: + for metric_key, metric_val in metrics.items(): + if isinstance(metric_val, list) or isinstance(metric_val, np.ndarray): + rounded = [f'{elem:.3f}' for elem in metric_val] + print('{}: {}'.format(colored(metric_key, 'yellow'), rounded)) + else: + print('{}: {:.3f}'.format(colored(metric_key, 'yellow'), metric_val)) + print('Evaluation done.') + return dict(trajs_data), metrics + + def _execute_evaluations(self, n_episodes=None, n_steps=None, log_freq=None, seeds=None): + '''Runs the experiments and collects all the required data. + + Args: + n_episodes (int): Number of runs to execute. + n_steps (int): The number of steps to collect in total. + log_freq (int): The frequency with which to log information. + seeds (list): An optional list of seeds for each episode. + + Returns: + trajs_data (defaultdict(list)): The raw data from the executed runs. + ''' + + if n_episodes is None and n_steps is None: + raise ValueError('One of n_episodes or n_steps must be defined.') + elif n_episodes is not None and n_steps is not None: + raise ValueError('Only one of n_episodes or n_steps can be defined.') + if seeds is not None: + assert len(seeds) == n_episodes, 'Number of seeds must match the number of episodes' + + # initialize + sim_steps = log_freq // self.env.CTRL_FREQ if log_freq else 1 + steps, trajs = 0, 0 + if seeds is not None: + seed = seeds[0] + else: + seed = None + obs, info = self._evaluation_reset(ctrl_data=None, sf_data=None, seed=seed) + ctrl_data = defaultdict(list) + sf_data = defaultdict(list) + + if n_episodes is not None: + while trajs < n_episodes: + action = self._select_action(obs=obs, info=info) + # inner sim loop to accomodate different control frequencies + for _ in range(sim_steps): + obs, _, done, info = self.env.step(action) + if done: + trajs += 1 + if trajs < n_episodes and seeds is not None: + seed = seeds[trajs] + obs, info = self._evaluation_reset(ctrl_data=ctrl_data, sf_data=sf_data) + break + elif n_steps is not None: + while steps < n_steps: + action = self._select_action(obs=obs, info=info) + # inner sim loop to accomodate different control frequencies + for _ in range(sim_steps): + obs, _, done, info = self.env.step(action) + steps += 1 + if steps >= n_steps: + self.env.save_data() + for data_key, data_val in self.ctrl.results_dict.items(): + ctrl_data[data_key].append(np.array(deepcopy(data_val))) + if self.safety_filter is not None: + for data_key, data_val in self.safety_filter.results_dict.items(): + sf_data[data_key].append(np.array(deepcopy(data_val))) + break + if done: + obs, info = self._evaluation_reset(ctrl_data=ctrl_data, sf_data=sf_data) + break + + trajs_data = self.env.data + trajs_data['controller_data'].append(munchify(dict(ctrl_data))) + if self.safety_filter is not None: + trajs_data['safety_filter_data'].append(munchify(dict(sf_data))) + return munchify(trajs_data) + + def _select_action(self, obs, info): + '''Determines the executed action using the controller and safety filter. + + Args: + obs (ndarray): The observation at this timestep. + info (dict): The info at this timestep. + + Returns: + action (ndarray): The action chosen by the controller and safety filter. + ''' + action = self.ctrl.select_action(obs, info) + + if self.safety_filter is not None: + physical_action = self.env.denormalize_action(action) + unextended_obs = obs[:self.env.symbolic.nx] + certified_action, _ = self.safety_filter.certify_action(unextended_obs, physical_action, info) + action = self.env.normalize_action(certified_action) + + return action + + def _evaluation_reset(self, ctrl_data, sf_data, seed=None): + '''Resets the evaluation between runs. + + Args: + ctrl_data (defaultdict): The controller specific data collected during execution. + sf_data (defaultdict): The safety filter specific data collected during execution. + seed (int): An optional seed to reset the environment. + + Returns: + obs (ndarray): The initial observation. + info (dict): The initial info. + ''' + if self.env.INFO_IN_RESET: + obs, info = self.env.reset(seed=seed) + else: + obs = self.env.reset(seed=seed) + info = None + if ctrl_data is not None: + for data_key, data_val in self.ctrl.results_dict.items(): + ctrl_data[data_key].append(np.array(deepcopy(data_val))) + if sf_data is not None and self.safety_filter is not None: + for data_key, data_val in self.safety_filter.results_dict.items(): + sf_data[data_key].append(np.array(deepcopy(data_val))) + self.ctrl.reset_before_run(obs, info, env=self.env) + if self.safety_filter is not None: + self.safety_filter.reset_before_run(env=self.env) + return obs, info + + def launch_training(self, **kwargs): + '''Since the learning loop varies among controllers, can only delegate to its own `learn()` method. + + Returns: + trajs_data (defaultdict(list)): The raw data from the training. + ''' + + self.reset() + self.ctrl.learn(env=self.train_env, **kwargs) + + if self.safety_filter: + self.safety_filter.learn(env=self.train_env, **kwargs) + + print('Training done.') + + trajs_data = {} + if self.train_env is not None: + trajs_data = self.train_env.data + return dict(trajs_data) + + def compute_metrics(self, trajs_data): + '''Compute all standard metrics on the given trajectory data. + + Args: + trajs_data (defaultdict(list)): The raw data from the executed runs. + + Returns: + metrics (dict): The metrics calculated from the raw data. + ''' + + metrics = self.metric_extractor.compute_metrics(data=trajs_data, verbose=self.verbose) + + return metrics + + def reset(self): + '''Resets the environments, controller, and safety filter to prepare for training or evaluation.''' + + self.env.reset() + self.env.clear_data() + self.ctrl.reset() + + if self.safety_filter is not None: + self.safety_filter.reset() + + if self.train_env is not None: + self.train_env.reset() + self.train_env.clear_data() + + def close(self): + '''Closes the environments, controller, and safety filter.''' + + self.env.close() + self.ctrl.close() + + if self.safety_filter is not None: + self.safety_filter.close() + + if self.train_env is not None: + self.train_env.close() + + def load(self, ctrl_path=None, safety_filter_path=None): + '''Restores model of the controller and/or safety filter given checkpoint paths. + + Args: + ctrl_path (str): The path used to load the controller's model. + safety_filter_path (str): The path used to load the safety_filter's model. + ''' + + if ctrl_path is not None: + self.ctrl.load(ctrl_path) + if safety_filter_path is not None: + self.safety_filter.load(safety_filter_path) + + def save(self, ctrl_path=None, safety_filter_path=None): + '''Saves the model of the controller and/or safety filter given checkpoint paths. + + Args: + ctrl_path (str): The path used to save the controller's model. + safety_filter_path (str): The path used to save the safety_filter's model. + ''' + + if ctrl_path is not None: + self.ctrl.save(ctrl_path) + if safety_filter_path is not None: + self.safety_filter.save(safety_filter_path) + + +class RecordDataWrapper(gym.Wrapper): + '''A wrapper to standardizes logging for benchmark envs. + + currently saved info + * obs, reward, done, info, action + * env.state, env.current_physical_action, + env.current_noisy_physical_action, env.current_clipped_action + ''' + + def __init__(self, env): + super().__init__(env) + self.episode_data = defaultdict(list) + self.clear_data() + + def save_data(self): + '''Saves the current self.episode_data to self.data and clears self.episode_data.''' + if self.episode_data: + # save to data container + for key, ep_val in self.episode_data.items(): + if key == 'info': + self.data[key].append(np.array(deepcopy(ep_val), dtype=object)) + else: + self.data[key].append(np.array(deepcopy(ep_val))) + # re-initialize episode data container + self.episode_data = defaultdict(list) + + def clear_data(self): + '''Clears all data in self.data and self.episode_data.''' + self.data = defaultdict(list) + self.episode_data = defaultdict(list) + + def reset(self, **kwargs): + '''Wrapper for the gym.env reset function.''' + + if self.env.INFO_IN_RESET: + obs, info = self.env.reset(**kwargs) + if 'symbolic_model' in info: + info.pop('symbolic_model') + if 'symbolic_constraints' in info: + info.pop('symbolic_constraints') + step_data = dict( + obs=obs, info=info, state=self.env.state + ) + for key, val in step_data.items(): + self.episode_data[key].append(val) + return obs, info + else: + obs = self.env.reset(**kwargs) + step_data = dict( + obs=obs, state=self.env.state + ) + for key, val in step_data.items(): + self.episode_data[key].append(val) + return obs + + def step(self, action): + '''Wrapper for the gym.env step function.''' + + obs, reward, done, info = self.env.step(action) + # save to episode data container + step_data = dict( + obs=obs, + action=self.env.current_raw_action, + done=float(done), + info=info, + reward=reward, + length=1, + state=self.env.state, + current_physical_action=self.env.current_physical_action, + current_noisy_physical_action=self.env.current_noisy_physical_action, + current_clipped_action=self.env.current_clipped_action, + timestamp=time(), + ) + for key, val in step_data.items(): + self.episode_data[key].append(val) + + if done: + self.save_data() + + return obs, reward, done, info + + +class MetricExtractor: + '''A utility class that computes metrics given collected trajectory data. + + metrics that can be derived + * episode lengths, episode total rewards/returns + * RMSE (given the square error/mse is saved in info dict at each step) + * episode occurrences of constraint violation + (0/1 for each episode, failure rate = #occurrences/#episodes) + * episode constraint violation steps + (how many constraint violations happened in each episode) + ''' + + def compute_metrics(self, data, verbose=False): + '''Compute all standard metrics on the given trajectory data. + + Args: + data (defaultdict(list)): The raw data from the executed runs. + verbose (bool, optional): If to suppress compute_metrics print statetments. + + Returns: + metrics (dict): The metrics calculated from the raw data. + ''' + + self.data = data + self.verbose = verbose + + # collect & compute all sorts of metrics here + metrics = { + 'average_length': np.asarray(self.get_episode_lengths()).mean(), + 'length': self.get_episode_lengths() if len(self.get_episode_lengths()) > 1 else self.get_episode_lengths()[0], + 'average_return': np.asarray(self.get_episode_returns()).mean(), + 'average_rmse': np.asarray(self.get_episode_rmse()).mean(), + 'rmse': np.asarray(self.get_episode_rmse()) if len(self.get_episode_rmse()) > 1 else self.get_episode_rmse()[0], + 'rmse_std': np.asarray(self.get_episode_rmse()).std(), + 'worst_case_rmse_at_0.5': compute_cvar(np.asarray(self.get_episode_rmse()), 0.5, lower_range=False), + 'failure_rate': np.asarray(self.get_episode_constraint_violations()).mean(), + 'average_constraint_violation': np.asarray(self.get_episode_constraint_violation_steps()).mean(), + 'constraint_violation_std': np.asarray(self.get_episode_constraint_violation_steps()).std(), + 'constraint_violation': np.asarray(self.get_episode_constraint_violation_steps()) if len(self.get_episode_constraint_violation_steps()) > 1 else self.get_episode_constraint_violation_steps()[0], + # others ??? + } + return metrics + + def get_episode_data(self, key, postprocess_func=lambda x: x): + '''Extract data field from recorded trajectory data, optionally postprocess each episode data (e.g. get sum). + + Args: + key (str): The key of the data to retrieve. + postprocess_func (lambda): A function to process the outgoing data. + + Returns: + episode_data (list): The desired data. + ''' + + if key in self.data: + episode_data = [postprocess_func(ep_val) for ep_val in self.data[key]] + elif key in self.data['info'][0][-1]: + # if the data field is contained in step info dict + episode_data = [] + for ep_info in self.data['info']: + ep_info_data = [] + for info in ep_info: + if key in info: + ep_info_data.append(info.get(key)) + elif self.verbose: + print(f'[Warn] MetricExtractor.get_episode_data: key {key} not in info dict.') + episode_data.append(postprocess_func(ep_info_data)) + else: + raise KeyError(f'Given data key \'{key}\' does not exist in recorded trajectory data.') + return episode_data + + def get_episode_lengths(self): + '''Total length of episodes. + + Returns: + episode_lengths (list): The lengths of each episode. + ''' + return self.get_episode_data('length', postprocess_func=sum) + + def get_episode_returns(self): + '''Total reward/return of episodes. + + Returns: + episode_rewards (list): The total reward of each episode. + ''' + return self.get_episode_data('reward', postprocess_func=sum) + + def get_episode_rmse(self): + '''Root mean square error of episodes. + + Returns: + episode_rmse (list): The total rmse of each episode. + ''' + return self.get_episode_data('mse', + postprocess_func=lambda x: float(np.sqrt(np.mean(x)))) + + def get_episode_constraint_violations(self): + '''Occurence of any violation in episodes. + + Returns: + episode_violated (list): Whether each episode had a constraint violation. + ''' + return self.get_episode_data('constraint_violation', + postprocess_func=lambda x: float(any(x))) + + def get_episode_constraint_violation_steps(self): + '''Total violation steps of episodes. + + Returns: + episode_violations (list): The total number of constraint violations of each episode. + ''' + return self.get_episode_data('constraint_violation', + postprocess_func=sum) diff --git a/safe_control_gym/experiments/train_rl_controller.py b/safe_control_gym/experiments/train_rl_controller.py new file mode 100644 index 000000000..15f2e5b6c --- /dev/null +++ b/safe_control_gym/experiments/train_rl_controller.py @@ -0,0 +1,76 @@ +'''Template training/plotting/testing script.''' + +import os +import shutil +from functools import partial + +import munch +import yaml + +from safe_control_gym.utils.configuration import ConfigFactory +from safe_control_gym.utils.plotting import plot_from_logs +from safe_control_gym.utils.registration import make +from safe_control_gym.utils.utils import (mkdirs, set_device_from_config, + set_seed_from_config) + + +def train(): + '''Training template. + + TODO: Add restore functionality + ''' + # Create the configuration dictionary. + fac = ConfigFactory() + config = fac.merge() + config.algo_config['training'] = True + + shutil.rmtree(config.output_dir, ignore_errors=True) + + set_seed_from_config(config) + set_device_from_config(config) + + # Define function to create task/env. + env_func = partial(make, + config.task, + output_dir=config.output_dir, + **config.task_config + ) + + # Create the controller/control_agent. + ctrl = make(config.algo, + env_func, + checkpoint_path=os.path.join(config.output_dir, 'model_latest.pt'), + output_dir=config.output_dir, + use_gpu=config.use_gpu, + seed=config.seed, + **config.algo_config) + ctrl.reset() + + # Training. + ctrl.learn() + ctrl.close() + print('Training done.') + + with open(os.path.join(config.output_dir, 'config.yaml'), 'w', encoding='UTF-8') as file: + yaml.dump(munch.unmunchify(config), file, default_flow_style=False) + + make_plots(config) + + +def make_plots(config): + '''Produces plots for logged stats during training. + Usage + * use with `--func plot` and `--restore {dir_path}` where `dir_path` is + the experiment folder containing the logs. + * save figures under `dir_path/plots/`. + ''' + # Define source and target log locations. + log_dir = os.path.join(config.output_dir, 'logs') + plot_dir = os.path.join(config.output_dir, 'plots') + mkdirs(plot_dir) + plot_from_logs(log_dir, plot_dir, window=3) + print('Plotting done.') + + +if __name__ == '__main__': + train() diff --git a/safe_control_gym/math_and_models/distributions.py b/safe_control_gym/math_and_models/distributions.py index bf2507451..81ee3c381 100644 --- a/safe_control_gym/math_and_models/distributions.py +++ b/safe_control_gym/math_and_models/distributions.py @@ -1,72 +1,60 @@ -"""Probability distributions for PyTorch. +'''Probability distributions for PyTorch. Based on https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/distributions.py +''' -""" import torch class Normal(torch.distributions.Normal): - """Multivariate Gaussian distribution given mean and std tensors. - - """ + '''Multivariate Gaussian distribution given mean and std tensors.''' def log_prob(self, actions): - """Log probability of actions given current distribution. + '''Log probability of actions given current distribution. Args: actions (torch.FloatTensor): shape (batch, *). Returns: (torch.FloatTensor): shape (batch, 1). - - """ + ''' return super().log_prob(actions).sum(-1, keepdim=True) def entropy(self): - """Entropy of current distribution. + '''Entropy of current distribution. Returns: (torch.FloatTensor): shape (batch,). - - """ + ''' return super().entropy().sum(-1) def mode(self): - """Mode (max probability point) of current distribution. - - """ + '''Mode (max probability point) of current distribution.''' return self.mean class Categorical(torch.distributions.Categorical): - """Categorical distribution given class probabilities or logits, not differentiable. - - """ + '''Categorical distribution given class probabilities or logits, not differentiable.''' def sample(self): - """Sample from the current distribution. + '''Sample from the current distribution. Returns: (torch.FloatTensor): shape (batch, 1). - - """ + ''' return super().sample().unsqueeze(-1) def log_prob(self, actions): - """Log probability of actions given current distribution. + '''Log probability of actions given current distribution. Args: actions (torch.FloatTensor): shape (batch, *). Returns: (torch.FloatTensor): shape (batch, 1). - - """ + ''' return (super().log_prob(actions.squeeze(-1)).view(actions.size(0), -1).sum(-1).unsqueeze(-1)) def mode(self): - """Mode (max probability point) of current distribution. - - """ + '''Mode (max probability point) of current distribution.''' return self.probs.argmax(dim=-1, keepdim=True) diff --git a/safe_control_gym/math_and_models/metrics/__init__.py b/safe_control_gym/math_and_models/metrics/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/safe_control_gym/math_and_models/metrics/performance_metrics.py b/safe_control_gym/math_and_models/metrics/performance_metrics.py new file mode 100644 index 000000000..a95c139db --- /dev/null +++ b/safe_control_gym/math_and_models/metrics/performance_metrics.py @@ -0,0 +1,36 @@ +'''Methods to calculate various metrics.''' + +import numpy as np + + +def compute_cvar(data, alpha, lower_range=True): + '''CVaR as mean of the lower-alpha-percentile of data. + adapted from https://github.com/nuria95/O-RAAC/blob/57347bc682798ff9f5600131c606517832efe864/oraaclib/util/utilities.py + + Args: + data (ndarray): the trajectory RMSE collected by the Experiment class + alpha (float): the percentile upper bound to use + lower_range (bool): ??? + + Returns: + cvar (float): the resulting CVaR + ''' + + data = np.atleast_2d(data) + _, N = data.shape + sorted_data = np.sort(data) + + # NOTE: what does it do? + # if alpha == 1 or alpha <= 0.5: + # cvar = sorted_data[:, :int(alpha * N)].mean(1) + # else: + # cvar = sorted_data[:, int(alpha * N)::].mean(1) + if lower_range: + cvar = sorted_data[:, :int(alpha * N)].mean() + else: + cvar = sorted_data[:, -int(alpha * N):].mean() + if np.all(np.isnan(cvar)): + raise ValueError(f'Not enough samples to compute {alpha} ' + f'CVaR from {data}') + else: + return cvar diff --git a/safe_control_gym/math_and_models/neural_networks.py b/safe_control_gym/math_and_models/neural_networks.py index 1b92c69cb..daf4759c3 100644 --- a/safe_control_gym/math_and_models/neural_networks.py +++ b/safe_control_gym/math_and_models/neural_networks.py @@ -1,43 +1,33 @@ -"""Neural networks. +'''Neural networks.''' -""" -import numpy as np import torch import torch.nn as nn import torch.nn.functional as F def get_activation(name): - """ - - """ return getattr(F, name) if name else lambda x: x def init_(module): - """ - - """ nn.init.orthogonal_(module.weight.data, gain=1) nn.init.constant_(module.bias.data, 0) return module class MLP(nn.Module): - """MLP network (can be used as value or policy). - - """ + '''MLP network (can be used as value or policy).''' def __init__(self, input_dim, output_dim, hidden_dims=[], - act="relu", + act='relu', output_act=None, init_weights=False, **kwargs ): - """Multi-layer perception/fully-connected network. + '''Multi-layer perception/fully-connected network. Args: input_dim (int): input dimension. @@ -45,8 +35,7 @@ def __init__(self, hidden_dims (list): hidden layer dimensions. act (str): hidden layer activation. output_act (str): output layer activation. - - """ + ''' super(MLP, self).__init__() dims = [input_dim] + hidden_dims + [output_dim] init_func = init_ if init_weights else lambda x: x @@ -58,9 +47,6 @@ def __init__(self, self.output_act = get_activation(output_act) def forward(self, x): - """ - - """ out = x for fc in self.fcs[:-1]: out = self.act(fc(out)) @@ -69,20 +55,15 @@ def forward(self, x): class CNN(nn.Module): - """CNN network for encoding images. - - """ + '''CNN network for encoding images.''' def __init__(self, input_dim, output_dim, - act="relu", - output_act="relu", + act='relu', + output_act='relu', **kwargs ): - """ - - """ super(CNN, self).__init__() self.convs = nn.ModuleList([ init_(nn.Conv2d(input_dim, 32, 8, stride=4)), @@ -95,9 +76,6 @@ def __init__(self, self.output_act = get_activation(output_act) def forward(self, x, normalize=False): - """ - - """ out = x / 255.0 if normalize else x N = x.shape[0] for conv in self.convs: @@ -107,18 +85,13 @@ def forward(self, x, normalize=False): class RNN(nn.Module): - """RNN network (can be used as value or policy). - - """ + '''RNN network (can be used as value or policy).''' def __init__(self, input_dim, output_dim, **kwargs ): - """ - - """ super(RNN, self).__init__() self.gru = nn.GRU(input_dim, output_dim) for name, param in self.gru.named_parameters(): @@ -128,9 +101,6 @@ def __init__(self, nn.init.orthogonal_(param) def forward(self, x, hxs, masks): - """ - - """ if x.size(0) == hxs.size(0): # Forward one step, x, hxs, masks: (N, *). x, hxs = self.gru(x.unsqueeze(0), (hxs * masks).unsqueeze(0)) @@ -139,7 +109,7 @@ def forward(self, x, hxs, masks): hxs = hxs.squeeze(0) else: # Forward a sequence, x, masks: (T, N, *), hxs: (N, *). - T, N = x.shape[:2] + T, _ = x.shape[:2] # Let's figure out which steps in the sequence have a zero for any agent. # We will always assume t=0 has a zero in it as that makes the logic cleaner. has_zeros = (masks.squeeze(-1)[1:] == 0.0).any(dim=-1).nonzero() diff --git a/safe_control_gym/math_and_models/normalization.py b/safe_control_gym/math_and_models/normalization.py index 4893210d0..e70c7197c 100644 --- a/safe_control_gym/math_and_models/normalization.py +++ b/safe_control_gym/math_and_models/normalization.py @@ -1,57 +1,48 @@ -"""Perform normalization on inputs or rewards. +'''Perform normalization on inputs or rewards.''' -""" import numpy as np import torch - -from gym.spaces import Box +from gymnasium.spaces import Box def normalize_angle(x): - """Wraps input angle to [-pi, pi]. - - """ + '''Wraps input angle to [-pi, pi].''' return ((x + np.pi) % (2 * np.pi)) - np.pi class RunningMeanStd(): - """Calulates the running mean and std of a data stream. - + '''Calulates the running mean and std of a data stream. + Attributes: mean (np.array): mean of data stream. var (np.array): variance of data stream. count (float): total count of data steam. - - """ + ''' def __init__(self, epsilon=1e-4, shape=()): - """Initializes containers for data mean and variance. - + '''Initializes containers for data mean and variance. + Args: epsilon (float): helps with arithmetic issues. shape (tuple): the shape of the data stream's output. - - """ + ''' self.mean = np.zeros(shape, np.float64) self.var = np.ones(shape, np.float64) self.count = epsilon def update(self, arr): - """Update current stats with a new stream of data. + '''Update current stats with a new stream of data. Args: arr (np.array): 1D array of data, (batch_size, *shape). - - """ + ''' batch_mean = np.mean(arr, axis=0) batch_var = np.var(arr, axis=0) batch_count = arr.shape[0] self.update_from_moments(batch_mean, batch_var, batch_count) def update_from_moments(self, batch_mean, batch_var, batch_count): - """Util function for `update` method. - - """ + '''Util function for `update` method.''' delta = batch_mean - self.mean tot_count = self.count + batch_count new_mean = self.mean + delta * batch_count / tot_count @@ -66,12 +57,11 @@ def update_from_moments(self, batch_mean, batch_var, batch_count): class BaseNormalizer(object): - """Template/default normalizer. + '''Template/default normalizer. Attributes: read_only (bool): if to freeze the current stats being tracked. - - """ + ''' def __init__(self, read_only=False): self.read_only = read_only @@ -83,39 +73,30 @@ def unset_read_only(self): self.read_only = False def __call__(self, x, *args, **kwargs): - """Invokes normalization on the given input. - - """ + '''Invokes normalization on the given input.''' return x def state_dict(self): - """Returns snapshot of current stats. - - """ + '''Returns snapshot of current stats.''' return {} def load_state_dict(self, _): - """Restores the stats from a snapshot. - - """ + '''Restores the stats from a snapshot.''' pass class MeanStdNormalizer(BaseNormalizer): - """Normalize by the running average. - - """ + '''Normalize by the running average.''' def __init__(self, shape=(), read_only=False, clip=10.0, epsilon=1e-8): - """Initializes the data stream tracker. + '''Initializes the data stream tracker. Args: shape (tuple): shape of data being tracked. read_only (bool): if to freeze the tracker. - clip (float): bounds on the data. + clip (float): bounds on the data. epsilon (float): offset to provide divide-by-zero. - - """ + ''' super().__init__(read_only) self.read_only = read_only self.rms = RunningMeanStd(shape=shape) @@ -123,9 +104,7 @@ def __init__(self, shape=(), read_only=False, clip=10.0, epsilon=1e-8): self.epsilon = epsilon def __call__(self, x): - """Update tracker given data, optionally normalize the data. - - """ + '''Update tracker given data, optionally normalize the data.''' x = np.asarray(x) if not self.read_only: self.rms.update(x) @@ -142,36 +121,32 @@ def load_state_dict(self, saved): class RewardStdNormalizer(MeanStdNormalizer): - """Reward normalization by running average of returns. - + '''Reward normalization by running average of returns. + Papers: - * arxiv.org/pdf/1808.04355.pdf - * arxiv.org/pdf/1810.12894.pdf + * arxiv.org/pdf/1808.04355.pdf + * arxiv.org/pdf/1810.12894.pdf Also see: * github.com/openai/baselines/issues/538 - - """ + ''' def __init__(self, gamma=0.99, read_only=False, clip=10.0, epsilon=1e-8): - """Initializes the data stream tracker. - + '''Initializes the data stream tracker. + Args: gamma (float): discount factor for rewards. read_only (bool): if to freeze the tracker. - clip (float): bounds on the data. + clip (float): bounds on the data. epsilon (float): offset to provide divide-by-zero. - - """ + ''' # Reward has default shape (1,) or just (). super().__init__((), read_only, clip, epsilon) self.gamma = gamma self.ret = None def __call__(self, x, dones): - """Update tracker given reward, optionally normalize the reward (only scaling). - - """ + '''Update tracker given reward, optionally normalize the reward (only scaling).''' x = np.asarray(x) if not self.read_only: # Track running average of forward discounted returns. @@ -185,56 +160,43 @@ def __call__(self, x, dones): class RescaleNormalizer(BaseNormalizer): - """Apply constant scaling. - - """ + '''Apply constant scaling.''' def __init__(self, coef=1.0): - """Initializes with fixed scaling constant. + '''Initializes with fixed scaling constant. Args: coef (float): scaling coefficient. - - """ + ''' super().__init__(self) self.coef = coef def __call__(self, x): - """Scale the input. - - """ + '''Scale the input.''' if not isinstance(x, torch.Tensor): x = np.asarray(x) return self.coef * x class ImageNormalizer(RescaleNormalizer): - """Scale image pixles from [0,255] to [0,1]. - - """ + '''Scale image pixles from [0,255] to [0,1].''' def __init__(self): super().__init__(self, 1.0 / 255) class ActionUnnormalizer(BaseNormalizer): - """Assumes policy output action is in [-1,1], unnormalize it for gym env. - - """ + '''Assumes policy output action is in [-1,1], unnormalize it for gym env.''' def __init__(self, action_space): - """Defines the mean and std for the bounded action space. - - """ + '''Defines the mean and std for the bounded action space.''' super().__init__() - assert isinstance(action_space, Box), "action space must be gym.spaces.Box" + assert isinstance(action_space, Box), 'action space must be gym.spaces.Box' low, high = action_space.low, action_space.high self.mean = (low + high) / 2.0 self.std = (high - low) / 2.0 def __call__(self, action): - """Unnormalizes given input action. - - """ + '''Unnormalizes given input action.''' x = np.asarray(action) return self.mean + x * self.std diff --git a/safe_control_gym/math_and_models/random_processes.py b/safe_control_gym/math_and_models/random_processes.py index 759acfaa3..7eee90e78 100644 --- a/safe_control_gym/math_and_models/random_processes.py +++ b/safe_control_gym/math_and_models/random_processes.py @@ -10,12 +10,12 @@ class RandomProcess(object): def reset_states(self): pass - + def state_dict(self): return {} - + def load_state_dict(self, state): - pass + pass class GaussianProcess(RandomProcess): @@ -47,9 +47,8 @@ def reset_states(self): self.x_prev = self.x0 if self.x0 is not None else np.zeros(self.size) def state_dict(self): - return {"x_prev": self.x_prev, "std": self.std.state_dict()} - + return {'x_prev': self.x_prev, 'std': self.std.state_dict()} + def load_state_dict(self, state): - self.x_prev = state["x_prev"] - self.std.load_state_dict(state["std"]) - \ No newline at end of file + self.x_prev = state['x_prev'] + self.std.load_state_dict(state['std']) diff --git a/safe_control_gym/math_and_models/schedule.py b/safe_control_gym/math_and_models/schedule.py index d8a962d28..14be6602b 100644 --- a/safe_control_gym/math_and_models/schedule.py +++ b/safe_control_gym/math_and_models/schedule.py @@ -4,18 +4,19 @@ # declaration at the top # ####################################################################### + class ConstantSchedule: def __init__(self, val): self.val = val def __call__(self, steps=1): return self.val - + def state_dict(self): return {} - + def load_state_dict(self, state): - pass + pass class LinearSchedule: @@ -35,9 +36,9 @@ def __call__(self, steps=1): val = self.current self.current = self.bound(self.current + self.inc * steps, self.end) return val - + def state_dict(self): - return {"current": self.current} - + return {'current': self.current} + def load_state_dict(self, state): - self.current = state["current"] + self.current = state['current'] diff --git a/safe_control_gym/math_and_models/symbolic_systems.py b/safe_control_gym/math_and_models/symbolic_systems.py index 286cffd7e..0bcc50f70 100644 --- a/safe_control_gym/math_and_models/symbolic_systems.py +++ b/safe_control_gym/math_and_models/symbolic_systems.py @@ -1,40 +1,36 @@ -"""Symbolic Models. +'''Symbolic Models.''' -""" -import numpy as np import casadi as cs class SymbolicModel(): - """Implements the dynamics model with symbolic variables. + '''Implements the dynamics model with symbolic variables. x_dot = f(x,u), y = g(x,u), with other pre-defined, symbolic functions (e.g. cost, constraints), serve as priors for the controllers. Notes: - * naming convention on symbolic variable and functions. + * naming convention on symbolic variable and functions. * for single-letter symbol, use {}_sym, otherwise use underscore for delimiter. * for symbolic functions to be exposed, use {}_func. - - """ + ''' def __init__(self, dynamics, cost, dt=1e-3, integration_algo='cvodes', - funcs=None): - """ - - """ + funcs=None, + params=None, + ): # Setup for dynamics. - self.x_sym = dynamics["vars"]["X"] - self.u_sym = dynamics["vars"]["U"] - self.x_dot = dynamics["dyn_eqn"] - if dynamics["obs_eqn"] is None: + self.x_sym = dynamics['vars']['X'] + self.u_sym = dynamics['vars']['U'] + self.x_dot = dynamics['dyn_eqn'] + if dynamics['obs_eqn'] is None: self.y_sym = self.x_sym else: - self.y_sym = dynamics["obs_eqn"] + self.y_sym = dynamics['obs_eqn'] # Sampling time. self.dt = dt # Integration algorithm. @@ -44,40 +40,42 @@ def __init__(self, for name, func in funcs.items(): assert name not in self.__dict__ self.__dict__[name] = func + # Cache other parameters, for example, X_EQ and U_EQ + # that would be used in the controller via the symbolic model + if params is not None: + for name, param in params.items(): + assert name not in self.__dict__ + self.__dict__[name] = param # Variable dimensions. self.nx = self.x_sym.shape[0] self.nu = self.u_sym.shape[0] self.ny = self.y_sym.shape[0] # Setup cost function. - self.cost_func = cost["cost_func"] - print(self.cost_func) - self.Q = cost["vars"]["Q"] - self.R = cost["vars"]["R"] - self.Xr = cost["vars"]["Xr"] - self.Ur = cost["vars"]["Ur"] + self.cost_func = cost['cost_func'] + # print(self.cost_func) + self.Q = cost['vars']['Q'] + self.R = cost['vars']['R'] + self.Xr = cost['vars']['Xr'] + self.Ur = cost['vars']['Ur'] # Setup symbolic model. self.setup_model() # Setup Jacobian and Hessian of the dynamics and cost functions. self.setup_linearization() def setup_model(self): - """Exposes functions to evaluate the model. - - """ + '''Exposes functions to evaluate the model.''' # Continuous time dynamics. self.fc_func = cs.Function('fc', [self.x_sym, self.u_sym], [self.x_dot], ['x', 'u'], ['f']) # Discrete time dynamics. self.fd_func = cs.integrator('fd', self.integration_algo, {'x': self.x_sym, 'p': self.u_sym, 'ode': self.x_dot}, {'tf': self.dt} - ) + ) # Observation model. self.g_func = cs.Function('g', [self.x_sym, self.u_sym], [self.y_sym], ['x', 'u'], ['g']) def setup_linearization(self): - """Exposes functions for the linearized model. - - """ + '''Exposes functions for the linearized model.''' # Jacobians w.r.t state & input. self.dfdx = cs.jacobian(self.x_dot, self.x_sym) self.dfdu = cs.jacobian(self.x_dot, self.u_sym) diff --git a/safe_control_gym/safety_filters/__init__.py b/safe_control_gym/safety_filters/__init__.py new file mode 100644 index 000000000..2d53e550f --- /dev/null +++ b/safe_control_gym/safety_filters/__init__.py @@ -0,0 +1,15 @@ +'''Register safety filters.''' + +from safe_control_gym.utils.registration import register + +register(idx='linear_mpsc', + entry_point='safe_control_gym.safety_filters.mpsc.linear_mpsc:LINEAR_MPSC', + config_entry_point='safe_control_gym.safety_filters.mpsc:mpsc.yaml') + +register(idx='cbf', + entry_point='safe_control_gym.safety_filters.cbf.cbf:CBF', + config_entry_point='safe_control_gym.safety_filters.cbf:cbf.yaml') + +register(idx='cbf_nn', + entry_point='safe_control_gym.safety_filters.cbf.cbf_nn:CBF_NN', + config_entry_point='safe_control_gym.safety_filters.cbf:cbf_nn.yaml') diff --git a/safe_control_gym/safety_filters/base_safety_filter.py b/safe_control_gym/safety_filters/base_safety_filter.py new file mode 100644 index 000000000..277f2b809 --- /dev/null +++ b/safe_control_gym/safety_filters/base_safety_filter.py @@ -0,0 +1,31 @@ +'''Base class for safety filter.''' + +from abc import abstractmethod + +from safe_control_gym.controllers.base_controller import BaseController + + +class BaseSafetyFilter(BaseController): + '''Template for safety filter, implement the following methods as needed.''' + + @abstractmethod + def certify_action(self, + current_state, + uncertified_action, + info=None, + ): + '''Determines a safe action from the current state and proposed action. + + Args: + current_state (ndarray): Current state/observation. + uncertified_action (ndarray): The uncertified_controller's action. + info (dict): The info at this timestep. + + Returns: + certified_action (ndarray): The certified action. + success (bool): Whether the safety filtering was successful or not. + ''' + raise NotImplementedError + + def select_action(self, obs, info=None): + raise NotImplementedError('[ERROR] select_action is not and will not be implemented for safety filters.') diff --git a/safe_control_gym/safety_filters/cbf/__init__.py b/safe_control_gym/safety_filters/cbf/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/safe_control_gym/safety_filters/cbf/cbf.py b/safe_control_gym/safety_filters/cbf/cbf.py new file mode 100644 index 000000000..fd51e26cf --- /dev/null +++ b/safe_control_gym/safety_filters/cbf/cbf.py @@ -0,0 +1,336 @@ +'''Control barrier function (CBF) quadratic programming (QP) safety filter. + +Reference: + * [Control Barrier Functions: Theory and Applications](https://arxiv.org/abs/1903.11199) +''' + +from typing import Tuple + +import casadi as cs +import numpy as np + +from safe_control_gym.safety_filters.base_safety_filter import BaseSafetyFilter +from safe_control_gym.safety_filters.cbf.cbf_utils import cartesian_product, cbf_cartpole, linear_function + + +class CBF(BaseSafetyFilter): + '''Control Barrier Function Class.''' + + def __init__(self, + env_func, + slope: float = 0.1, + soft_constrained: bool = True, + slack_weight: float = 10000.0, + slack_tolerance: float = 1.0E-3, + **kwargs): + ''' + CBF-QP Safety Filter: The CBF's superlevel set defines a positively control invariant set. + A QP based on the CBF's Lie derivative with respect to the dynamics allows to filter arbitrary control + inputs to keep the system inside the CBF's superlevel set. + + Args: + env_func (partial BenchmarkEnv): Functionalized initialization of the environment. + slope (float): The slope of the linear function in the CBF. + soft_constrainted (bool): Whether to use soft or hard constraints. + slack_weight (float): The weight of the slack in the optimization. + slack_tolerance (float): How high the slack can be in the optimization. + ''' + + super().__init__(env_func=env_func, **kwargs) + self.env = self.env_func() + + self.slope = slope + self.soft_constrained = soft_constrained + self.slack_weight = slack_weight + self.slack_tolerance = slack_tolerance + + self.input_constraints_sym = self.env.constraints.get_input_constraint_symbolic_models() + self.state_constraints_sym = self.env.constraints.get_state_constraint_symbolic_models() + input_constraints = self.env.constraints.input_constraints + state_constraints = self.env.constraints.state_constraints + + if len(input_constraints) > 1: + raise NotImplementedError('CBF currently can\'t handle more than 1 constraint') + if len(input_constraints) == 0: + raise Exception('CBF requires at least 1 input constraint') + self.input_constraint = input_constraints[0] + + if len(state_constraints) > 1: + raise NotImplementedError('CBF currently can\'t handle more than 1 constraint') + if len(state_constraints) == 0: + raise Exception('CBF requires at least 1 state constraint') + self.state_constraint = state_constraints[0] + + self.reset() + self.X = self.model.x_sym + self.u = self.model.u_sym + + if self.env.NAME == 'cartpole': + self.state_limits = [min(abs(self.state_constraint.upper_bounds[i]), abs(self.state_constraint.lower_bounds[i])) for i in range(self.model.nx)] + self.cbf = cbf_cartpole(self.X, self.state_limits) + else: + raise NotImplementedError('[Error] Currently CBF is only implemented for the cartpole system.') + + # Check if the dynamics are control affine + assert self.is_control_affine() + + # Lie derivative with respect to the dynamics + self.lie_derivative = self.get_lie_derivative() + + self.linear_func = linear_function(self.slope) + + # Setup Optimizer + self.setup_optimizer() + + def get_lie_derivative(self) -> cs.Function: + '''Determines the Lie derivative of the CBF with respect to the known dynamics. + + Returns: + LfV_func (cs.Function): The lie derivative of the system. + ''' + dVdx = cs.gradient(self.cbf(X=self.X)['cbf'], self.X) + LfV = cs.dot(dVdx, self.model.x_dot) + LfV_func = cs.Function('LfV', [self.X, self.u], [LfV], ['X', 'u'], ['LfV']) + return LfV_func + + def is_control_affine(self) -> bool: + '''Check if the system is control affine. + + Returns: + control_affine (bool): Whether the system is control affine or not. + ''' + dfdu = cs.jacobian(self.model.x_dot, self.u) + return not cs.depends_on(dfdu, self.u) + + def setup_optimizer(self): + '''Setup the certifying CBF problem.''' + nx, nu = self.model.nx, self.model.nu + + # Define optimizer + opti = cs.Opti('conic') # Tell casadi that it's a conic problem + + # Optimization variable: control input + u_var = opti.variable(nu, 1) + + # States + current_state_var = opti.parameter(nx, 1) + uncertified_action_var = opti.parameter(nu, 1) + + # Evaluate at Lie derivative and CBF at the current state + lie_derivative_at_x = self.lie_derivative(X=current_state_var, u=u_var)['LfV'] + barrier_at_x = self.cbf(X=current_state_var)['cbf'] + + right_hand_side = 0.0 + slack_var = opti.variable(1, 1) + + if self.soft_constrained: + # Quadratic objective + cost = 0.5 * cs.norm_2(uncertified_action_var - u_var) ** 2 + self.slack_weight * slack_var**2 + + # Soften CBF constraint + right_hand_side = slack_var + + # Non-negativity constraint on slack variable + opti.subject_to(slack_var >= 0.0) + else: + opti.subject_to(slack_var == 0.0) + # Quadratic objective + cost = 0.5 * cs.norm_2(uncertified_action_var - u_var) ** 2 + + # CBF constraint + opti.subject_to(-self.linear_func(x=barrier_at_x)['y'] - lie_derivative_at_x <= right_hand_side) + + # Input constraints + for input_constraint in self.input_constraints_sym: + opti.subject_to(input_constraint(u_var) <= 0) + + opti.minimize(cost) + + # Set verbosity option of optimizer + opts = {'printLevel': 'low', 'error_on_fail': False} + + # Select QP solver + opti.solver('qpoases', opts) + + self.opti_dict = { + 'opti': opti, + 'u_var': u_var, + 'slack_var': slack_var, + 'current_state_var': current_state_var, + 'uncertified_action_var': uncertified_action_var, + 'cost': cost + } + + def solve_optimization(self, + current_state: np.ndarray, + uncertified_action: np.ndarray, + ) -> Tuple[np.ndarray, bool]: + '''Solve the CBF optimization problem for a given observation and uncertified input. + + Args: + current_state (np.ndarray): Current state/observation. + uncertified_action (np.ndarray): The uncertified_controller's action. + + Returns: + certified_action (np.ndarray): The certified action. + feasible (bool): Whether the safety filtering was feasible or not. + ''' + + opti_dict = self.opti_dict + opti = opti_dict['opti'] + u_var = opti_dict['u_var'] + slack_var = opti_dict['slack_var'] + current_state_var = opti_dict['current_state_var'] + uncertified_action_var = opti_dict['uncertified_action_var'] + cost = opti_dict['cost'] + + opti.set_value(current_state_var, current_state) + opti.set_value(uncertified_action_var, uncertified_action) + + try: + # Solve optimization problem + sol = opti.solve() + feasible = True + + certified_action = sol.value(u_var) + if self.soft_constrained: + slack_val = sol.value(slack_var) + if slack_val > self.slack_tolerance: + print('\nFailed: Slack greater than tolerance') + print('Slack:', slack_val) + print('------------------------------------------------') + feasible = False + cost = sol.value(cost) + except RuntimeError as e: + print(e) + feasible = False + certified_action = opti.debug.value(u_var) + print('Certified_action:', certified_action) + if self.soft_constrained: + slack_val = opti.debug.value(slack_var) + print('Slack:', slack_val) + print('Lie Derivative:', self.lie_derivative(X=current_state, u=certified_action)['LfV']) + print('Linear Function:', self.linear_func(x=self.cbf(X=current_state)['cbf'])['y']) + print('------------------------------------------------') + return certified_action, feasible + + def certify_action(self, + current_state: np.ndarray, + uncertified_action: np.ndarray, + info: dict = None + ) -> Tuple[np.ndarray, bool]: + '''Determines a safe action from the current state and proposed action. + + Args: + current_state (np.ndarray): Current state/observation. + uncertified_action (np.ndarray): The uncertified_controller's action. + info (dict): The info at this timestep. + + Returns: + certified_action (np.ndarray): The certified action. + success (bool): Whether the safety filtering was successful or not. + ''' + + uncertified_action = np.clip(uncertified_action, self.env.physical_action_bounds[0], self.env.physical_action_bounds[1]) + self.results_dict['uncertified_action'].append(uncertified_action) + certified_action, success = self.solve_optimization(current_state, uncertified_action) + self.results_dict['feasible'].append(success) + certified_action = np.squeeze(np.array(certified_action)) + self.results_dict['certified_action'].append(certified_action) + self.results_dict['correction'].append(np.linalg.norm(certified_action - uncertified_action)) + + return certified_action, success + + def is_cbf(self, + num_points: int = 100, + tolerance: float = 0.01 + ) -> Tuple[bool, list]: + ''' + Check if the provided CBF candidate is actually a CBF for the system using a gridded approach. + + Args: + num_points (int): The number of points in each dimension to check. + tolerance (float): The tolerance of the condition outside the superlevel set. + + Returns: + valid_cbf (bool): Whether the provided CBF candidate is valid. + infeasible_states (list): List of all states for which the QP is infeasible. + ''' + valid_cbf = False + epsilon = 1e-6 + + # Select the states to check the CBF condition + max_bounds = np.array(self.state_limits) + # Add some tolerance to the bounds to also check the condition outside of the superlevel set + max_bounds += tolerance + min_bounds = -max_bounds + + # state dimension and input dimension + nx, nu = self.model.nx, self.model.nu + + # Make sure that every vertex is checked + num_points = max(2 * nx, num_points + num_points % (2 * nx)) + num_points_per_dim = num_points // nx + + # Create the lists of states to check + states_to_sample = [np.linspace(min_bounds[i], max_bounds[i], num_points_per_dim) for i in range(nx)] + states_to_check = cartesian_product(*states_to_sample) + + # Set dummy control input + control_input = np.ones((nu, 1)) + + num_infeasible = 0 + num_infeasible_states_inside_set = 0 + infeasible_states = [] + + # Check if the optimization problem is feasible for every considered state + for state in states_to_check: + # Certify action + _, success = self.certify_action(state, control_input) + + if not success: + infeasible_states.append(state) + num_infeasible += 1 + barrier_at_x = self.cbf(X=state)['cbf'] + + # Check if the infeasible point is inside or outside the superlevel set. Note that the sampled region makes up a + # box, but the superlevel set is not. The superlevel set only needs to be contained inside the box. + if barrier_at_x > 0.0 + epsilon: + num_infeasible_states_inside_set += 1 + + print('Number of infeasible states:', num_infeasible) + print('Number of infeasible states inside superlevel set:', num_infeasible_states_inside_set) + + if num_infeasible_states_inside_set > 0: + valid_cbf = False + print('The provided CBF candidate is not a valid CBF.') + elif num_infeasible > 0: + valid_cbf = True + print('The provided CBF candidate is a valid CBF inside its superlevel set for the checked states. ' + 'Consider increasing the sampling resolution to get a more precise evaluation. ' + 'The CBF is not valid on the entire provided domain. Consider softening the CBF constraint by ' + 'setting \'soft_constraint: True\' inside the config.') + else: + valid_cbf = True + print('The provided CBF candidate is a valid CBF for the checked states. ' + 'Consider increasing the sampling resolution to get a more precise evaluation.') + + return valid_cbf, infeasible_states + + def setup_results_dict(self): + '''Setup the results dictionary to store run information.''' + self.results_dict = {} + self.results_dict['feasible'] = [] + self.results_dict['uncertified_action'] = [] + self.results_dict['certified_action'] = [] + self.results_dict['correction'] = [] + + def reset(self): + '''Resets the safety filter.''' + self.model = self.get_prior(self.env, self.prior_info) + self.env.reset() + self.setup_results_dict() + + def close(self): + '''Cleans up resources.''' + self.env.close() diff --git a/safe_control_gym/safety_filters/cbf/cbf.yaml b/safe_control_gym/safety_filters/cbf/cbf.yaml new file mode 100644 index 000000000..5c2cce301 --- /dev/null +++ b/safe_control_gym/safety_filters/cbf/cbf.yaml @@ -0,0 +1,11 @@ +slope: 0.1 + +soft_constrained: True +slack_weight: 10000.0 +slack_tolerance: 1.0E-3 + +# Prior info +prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null diff --git a/safe_control_gym/safety_filters/cbf/cbf_nn.py b/safe_control_gym/safety_filters/cbf/cbf_nn.py new file mode 100644 index 000000000..df19ea8dc --- /dev/null +++ b/safe_control_gym/safety_filters/cbf/cbf_nn.py @@ -0,0 +1,385 @@ +'''Control barrier function (CBF) quadratic programming (QP) safety filter with learned Lie derivatives + +Reference: + * [Learning for Safety-Critical Control with Control Barrier Functions](https://arxiv.org/abs/1912.10099) +''' + +import os +from typing import Tuple + +import casadi as cs +import numpy as np +import torch + +from safe_control_gym.math_and_models.neural_networks import MLP +from safe_control_gym.safety_filters.cbf.cbf import CBF +from safe_control_gym.safety_filters.cbf.cbf_utils import CBFBuffer + + +class CBF_NN(CBF): + '''Neural Network Based Control Barrier Function Class.''' + + def __init__(self, + env_func, + # CBF Parameters + slope: float = 0.1, + soft_constrained: bool = True, + slack_weight: float = 10000.0, + slack_tolerance: float = 1.0E-3, + # NN Parameters + max_num_steps: int = 250, + hidden_dims: list = None, + learning_rate: float = 0.001, + num_episodes: int = 20, + max_buffer_size: int = 1.0E+6, + train_batch_size: int = 64, + train_iterations: int = 200, + **kwargs): + ''' + CBF-QP safety filter with learned Lie derivative: The CBF's superlevel set defines a positively control invariant + set. A QP based on the CBF's Lie derivative with respect to the dynamics allows to filter arbitrary control + inputs to keep the system inside the CBF's superlevel set. Due to model mismatch, the Lie derivative is also + incorrect. This approach learns the error in the Lie derivative from multiple experiments to satisfy the + Lie derivative condition in the QP for the true system. + + Args: + env_func (BenchmarkEnv): Functionalized initialization of the environment. + slope (float): The slope of the linear function in the CBF. + soft_constrainted (bool): Whether to use soft or hard constraints. + slack_weight (float): The weight of the slack in the optimization. + slack_tolerance (float): How high the slack can be in the optimization. + max_num_steps (int): Maximum number of steps to train for. + hidden_dims (list): Number of hidden dimensions in the neural network. + learning_rate (float): The learning rate of the neural network. + num_episodes (int): Number of episodes to train for. + max_buffer_size (int): Maximum size of the CBFBuffer. + train_batch_size (int): Size of the batches used during training. + train_iterations (int): Number of iterations to update policy with batch. + ''' + + super().__init__(env_func, slope, soft_constrained, slack_weight, slack_tolerance, **kwargs) + + self.step_size = self.env.PYB_FREQ // self.env.CTRL_FREQ + + self.max_num_steps = max_num_steps + self.hidden_dims = hidden_dims + self.learning_rate = learning_rate + self.num_episodes = num_episodes + self.max_buffer_size = max_buffer_size + self.train_batch_size = train_batch_size + self.train_iterations = train_iterations + + # Neural network to learn the residual in the lie derivative + self.mlp = MLP(self.model.nx, self.model.nu + 1, hidden_dims=self.hidden_dims, activation='relu') + # Optimizer + self.opt = torch.optim.Adam(self.mlp.parameters(), self.learning_rate) + + max_buffer_size = int(self.max_buffer_size) + self.buffer = CBFBuffer(self.env.observation_space, self.env.action_space, max_buffer_size, self.device, + self.train_batch_size) + + self.uncertified_controller = None + self.setup_optimizer() + + def setup_optimizer(self): + '''Setup the certifying CBF problem.''' + nx, nu = self.model.nx, self.model.nu + + # Define optimizer + opti = cs.Opti('conic') # Tell casadi that it's a conic problem + + # Optimization variable: control input + u_var = opti.variable(nu, 1) + + # States + current_state_var = opti.parameter(nx, 1) + uncertified_action_var = opti.parameter(nu, 1) + a_var = opti.parameter(nu, 1) + b_var = opti.parameter(1, 1) + + # Evaluate at Lie derivative and CBF at the current state + lie_derivative_at_x = self.lie_derivative(X=current_state_var, u=u_var)['LfV'] + barrier_at_x = self.cbf(X=current_state_var)['cbf'] + + learned_residual = cs.dot(a_var.T, u_var) + b_var + + right_hand_side = 0.0 + slack_var = opti.variable(1, 1) + + if self.soft_constrained: + # Quadratic objective + cost = 0.5 * cs.norm_2(uncertified_action_var - u_var) ** 2 + self.slack_weight * slack_var**2 + + # Soften CBF constraint + right_hand_side = slack_var + + # Non-negativity constraint on slack variable + opti.subject_to(slack_var >= 0.0) + else: + opti.subject_to(slack_var == 0.0) + # Quadratic objective + cost = 0.5 * cs.norm_2(uncertified_action_var - u_var) ** 2 + + # CBF constraint + opti.subject_to(-self.linear_func(x=barrier_at_x)['y'] - lie_derivative_at_x - learned_residual <= right_hand_side) + + # Input constraints + for input_constraint in self.input_constraints_sym: + opti.subject_to(input_constraint(u_var) <= 0) + + opti.minimize(cost) + + # Set verbosity option of optimizer + opts = {'printLevel': 'low', 'error_on_fail': False} + + # Select QP solver + opti.solver('qpoases', opts) + + self.opti_dict = { + 'opti': opti, + 'u_var': u_var, + 'slack_var': slack_var, + 'current_state_var': current_state_var, + 'uncertified_action_var': uncertified_action_var, + 'a_var': a_var, + 'b_var': b_var, + 'cost': cost + } + + def solve_optimization(self, + current_state: np.ndarray, + uncertified_action: np.ndarray, + ) -> Tuple[np.ndarray, bool]: + '''Solve the CBF optimization problem for a given observation and uncertified input. + + Args: + current_state (np.ndarray): Current state/observation. + uncertified_action (np.ndarray): The uncertified_controller's action. + + Returns: + certified_action (np.ndarray): The certified action. + feasible (bool): Whether the safety filtering was feasible or not. + ''' + + opti_dict = self.opti_dict + opti = opti_dict['opti'] + u_var = opti_dict['u_var'] + slack_var = opti_dict['slack_var'] + current_state_var = opti_dict['current_state_var'] + uncertified_action_var = opti_dict['uncertified_action_var'] + a_var = opti_dict['a_var'] + b_var = opti_dict['b_var'] + cost = opti_dict['cost'] + + opti.set_value(current_state_var, current_state) + opti.set_value(uncertified_action_var, uncertified_action) + + a, b = self.extract_a_b(current_state) + + opti.set_value(a_var, a) + opti.set_value(b_var, b) + + try: + # Solve optimization problem + sol = opti.solve() + feasible = True + certified_action = sol.value(u_var) + if self.soft_constrained: + slack_val = sol.value(slack_var) + if slack_val > self.slack_tolerance: + print('Slack:', slack_val) + feasible = False + cost = sol.value(cost) + except RuntimeError as e: + print(e) + feasible = False + certified_action = opti.debug.value(u_var) + print('Certified_action:', certified_action) + if self.soft_constrained: + slack_val = opti.debug.value(slack_var) + print('Slack:', slack_val) + print('Lie Derivative:', self.lie_derivative(X=current_state, u=certified_action)['LfV']) + print('Linear Function:', self.linear_func(x=self.cbf(X=current_state)['cbf'])['y']) + print('------------------------------------------------') + return certified_action, feasible + + def extract_a_b(self, + current_state: np.ndarray + ) -> Tuple[np.ndarray, np.ndarray]: + '''Extracts the a and b vectors from the torch state. + + Args: + current_state (np.ndarray): The current state of the system. + + Returns: + a, b (np.ndarray): The a and b vectors used to calculate the learned residual. + ''' + torch_state = torch.from_numpy(current_state) + torch_state = torch.unsqueeze(torch_state, 0) + torch_state = torch_state.to(torch.float32) + a_b = self.mlp(torch_state) + a_b = a_b.detach().numpy() + a = a_b[0, :self.model.nu] + b = a_b[0, -1] + + return a, b + + def compute_loss(self, + batch: dict + ) -> float: + '''Compute training loss of the neural network that represents the Lie derivative error. + + Args: + batch (dict): The batch of data used during training. + + Returns: + loss (float): The loss of the batch. + ''' + state, act, barrier_dot, barrier_dot_approx = batch['state'], batch['act'], batch['barrier_dot'], \ + batch['barrier_dot_approx'] + + # Predict a and b vectors + a_b = self.mlp(state) + a = torch.unsqueeze(a_b[:, 0], 1) + b = torch.unsqueeze(a_b[:, 1], 1) + + # Determine the estimate of h_dot + h_dot_estimate = barrier_dot + a * act + b + + # Determine loss + loss = (h_dot_estimate - barrier_dot_approx).pow(2).mean() + return loss + + def update(self, + batch: dict + ): + '''Update the neural network parameters. + + Args: + batch (dict): The batch of data used during training. + ''' + loss = self.compute_loss(batch) + self.opt.zero_grad() + loss.backward() + self.opt.step() + + def reset(self): + '''Resets the safety filter.''' + super().reset() + if hasattr(self, 'buffer'): + self.buffer.reset() + + def save(self, + path: str + ): + '''Saves model params and experiment state to path. + + Args: + path (str): The path where to save the model params/experiment state. + ''' + path_dir = os.path.dirname(path) + os.makedirs(path_dir, exist_ok=True) + + state_dict = { + 'agent': self.mlp.state_dict() + } + if self.training: + exp_state = { + 'buffer': self.buffer.state_dict() + } + state_dict.update(exp_state) + torch.save(state_dict, path) + + def load(self, + path: str + ): + '''Restores model and experiment given path. + + Args: + path (str): The path where the model params/experiment state are saved. + ''' + state = torch.load(path) + + # Restore params + self.mlp.load_state_dict(state['agent']) + + # Restore experiment state + if self.training: + self.buffer.load_state_dict(state['buffer']) + + def learn(self, + env=None, + **kwargs + ): + '''Learn the error in the Lie derivative from multiple experiments. + + Args: + env (BenchmarkEnv): The environment to be used for training. + ''' + if env is None: + env = self.env + + input_blending_weight = np.arange(self.num_episodes) / (self.num_episodes - 1) + + # Run experiments in loop + for i in range(self.num_episodes): + # Reset the episode + obs, info = env.reset() + + counter = 0 + + # Create arrays to collect data + states = np.zeros((self.max_num_steps, self.model.nx)) + inputs = np.zeros((self.max_num_steps, self.model.nu)) + barrier_values = np.zeros((self.max_num_steps, 1)) + lie_derivative_values = np.zeros((self.max_num_steps, 1)) + lie_derivative_est = np.zeros((self.max_num_steps, 1)) + + while counter < self.max_num_steps: + print('Step: ', env.pyb_step_counter // self.step_size) + + # Determine safe action + if self.uncertified_controller is None: + uncertified_action = self.env.action_space.sample() + else: + uncertified_action = self.uncertified_controller.select_action(obs, info) + safe_action, _ = self.certify_action(obs, uncertified_action) + + # Blend the safe and uncertified action + blended_input = (1 - input_blending_weight[i]) * uncertified_action + input_blending_weight[i] * safe_action + + # Step the system + obs, _, _, info = env.step(blended_input) + print(f'obs: {obs}') + print(f'action: {safe_action}') + + # Collect data + states[counter, :] = obs + inputs[counter, :] = blended_input + barrier_values[counter, :] = self.cbf(X=obs)['cbf'] + lie_derivative_values[counter, :] = self.lie_derivative(X=obs, u=blended_input)['LfV'] + + # Determine the estimated Lie derivative + a, b = self.extract_a_b(current_state=obs) + lie_derivative_est[counter, :] = lie_derivative_values[counter, :] + np.dot(a.T, blended_input) + b + + counter += 1 + + print('Num time steps:', len(inputs)) + print('Certified control input weight:', input_blending_weight[i]) + + # Numerical time differentiation (symmetric) of barrier function: + barrier_dot_approx = (barrier_values[2:] - barrier_values[:-2]) / (2 * 1 / env.CTRL_FREQ) + + # Add data to buffer + self.buffer.push({ + 'state': states[1:-1, :], + 'act': inputs[1:-1, :], + 'barrier_dot': lie_derivative_values[1:-1, :], + 'barrier_dot_approx': barrier_dot_approx + }) + + # Update neural network parameters + for _ in range(self.train_iterations): + batch = self.buffer.sample(self.train_batch_size) + self.update(batch) diff --git a/safe_control_gym/safety_filters/cbf/cbf_nn.yaml b/safe_control_gym/safety_filters/cbf/cbf_nn.yaml new file mode 100644 index 000000000..7f6a36dd3 --- /dev/null +++ b/safe_control_gym/safety_filters/cbf/cbf_nn.yaml @@ -0,0 +1,22 @@ +slope: 0.1 + +soft_constrained: True +slack_weight: 10000.0 +slack_tolerance: 1.0E-3 + +# NN Parameters +max_num_steps: 250 + +hidden_dims: [256, 256] +learning_rate: 0.001 + +num_episodes: 20 +max_buffer_size: 1.0E+6 +train_batch_size: 64 +train_iterations: 200 + +# Prior info +prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null diff --git a/safe_control_gym/safety_filters/cbf/cbf_utils.py b/safe_control_gym/safety_filters/cbf/cbf_utils.py new file mode 100644 index 000000000..5ec648d1e --- /dev/null +++ b/safe_control_gym/safety_filters/cbf/cbf_utils.py @@ -0,0 +1,217 @@ +'''Utils for CBF and CBF_NN safety filters.''' + +import casadi as cs +import numpy as np +import torch +from gymnasium.spaces import Box, box + + +def cbf_cartpole(X: cs.MX, + state_limits: list + ) -> cs.Function: + '''Creates a control barrier function (CBF) candidate based on an ellipsoid function. + + Args: + X (cs.MX): Symbolic state variable. + state_limits (list): List of the limits on the state. + + Returns: + cbf_func (cs.Function): Function object of the control barrier function (CBF). + ''' + shrink_factor = 1.0 + x_max, v_max, theta_max, omega_max = state_limits + cbf = shrink_factor - (X[0] / x_max)**2 - (X[1] / v_max)**2 - (X[2] / theta_max)**2 - (X[3] / omega_max)**2 + # cbf = 0.5 * (1 - (X[0]) ** 2 / (x_max) ** 2) + # cbf = 0.5 * (1 - (X[2]) ** 2 / (theta_max) ** 2) + # cbf = 0.5 * (1 - (X[0]) ** 2 / (x_max) ** 2 - (X[3]) ** 2 / (omega_max) ** 2) + # cbf = 0.5 * (1 - (X[0]) ** 2 / (x_max) ** 2 - (X[1]) ** 2 / (v_max) ** 2) + cbf_func = cs.Function('barrier', [X], [cbf], ['X'], ['cbf']) + return cbf_func + + +def linear_function(slope: float) -> cs.Function: + '''Creates a one dimensional linear function. + + Args: + slope (float): The slope of the linear function. + + Returns: + linear_func (cs.Function): Function object of a linear function with desired slope. + ''' + x = cs.MX.sym('x', 1) + linear_func = cs.Function('linear', [x], [slope * x], ['x'], ['y']) + return linear_func + + +def cartesian_product(*arrays: list) -> np.ndarray: + '''Creates the cartesian product of a list of arrays from: + https://stackoverflow.com/a/11146645 + + Args: + arrays (list): List of arrays. + + Returns: + cartesian (np.ndarray): Cartesian product of all arrays in the list. + ''' + la = len(arrays) + dtype = np.result_type(*arrays) + arr = np.empty([len(a) for a in arrays] + [la], dtype=dtype) + for i, a in enumerate(np.ix_(*arrays)): + arr[..., i] = a + return arr.reshape(-1, la) + + +class CBFBuffer(object): + '''Storage for replay buffer during training. + + Attributes: + max_size (int): Maximum size of the replay buffer. + batch_size (int): Number of samples (steps) per batch. + scheme (dict): Describes shape and other info of data to be stored. + keys (list): Names of all data from scheme. + ''' + + def __init__(self, + obs_space: box.Box, + act_space: box.Box, + max_size: int, + device: str, + batch_size: int + ): + ''' + A buffer for CBF_NN. + + Args: + obs_space (gym.spaces.box.Box): The observation space. + act_space (gym.spaces.box.Box): The action space. + max_size (int): Maximum size of the replay buffer. + device (str): The device being used. + batch_size (int): Number of samples (steps) per batch. + ''' + + super().__init__() + self.max_size = max_size + self.batch_size = batch_size + + self.device = device + + obs_dim = obs_space.shape + if isinstance(act_space, Box): + act_dim = act_space.shape[0] + else: + act_dim = act_space.n + + N = max_size + self.scheme = { + 'state': { + 'vshape': (N, *obs_dim) + }, + 'act': { + 'vshape': (N, act_dim) + }, + 'barrier_dot': { + 'vshape': (N, 1) + }, + 'barrier_dot_approx': { + 'vshape': (N, 1) + } + } + self.keys = list(self.scheme.keys()) + self.reset() + + def reset(self): + '''Allocate space for containers.''' + for k, info in self.scheme.items(): + assert 'vshape' in info, f'Scheme must define vshape for {k}' + vshape = info['vshape'] + dtype = info.get('dtype', np.float32) + init = info.get('init', np.zeros) + self.__dict__[k] = init(vshape, dtype=dtype) + + self.pos = 0 + self.buffer_size = 0 + + def __len__(self) -> int: + '''Returns current size of the buffer. + + Returns: + buffer_size (int): The buffer size. + ''' + return self.buffer_size + + def state_dict(self) -> dict: + '''Returns a snapshot of current buffer. + + Returns: + state (dict): The state dictionary. + ''' + state = dict( + pos=self.pos, + buffer_size=self.buffer_size, + ) + for k in self.scheme: + v = self.__dict__[k] + state[k] = v + return state + + def load_state_dict(self, + state: dict, + ): + '''Restores buffer from previous state. + + Args: + state (dict): The state dictionary. + ''' + for k, v in state.items(): + self.__dict__[k] = v + + def push(self, + batch: dict, + ): + '''Inserts transition step data (as dict) to storage. + + Args: + batch (dict): The batch of data to push. + ''' + # batch size + k = list(batch.keys())[0] + n = batch[k].shape[0] + + for k, v in batch.items(): + shape = self.scheme[k]['vshape'][1:] + dtype = self.scheme[k].get('dtype', np.float32) + v_ = np.asarray(v, dtype=dtype).reshape((n,) + shape) + + if self.pos + n <= self.max_size: + self.__dict__[k][self.pos:self.pos + n] = v_ + else: + # wrap around + remain_n = self.pos + n - self.max_size + self.__dict__[k][self.pos:self.max_size] = v_[:-remain_n] + self.__dict__[k][:remain_n] = v_[-remain_n:] + + self.pos = (self.pos + n) % self.max_size + if self.buffer_size < self.max_size: + self.buffer_size = min(self.max_size, self.pos + n) + + def sample(self, + batch_size: int = None + ) -> dict: + '''Returns data batch. + + Args: + batch_size (int): The size of batch to sample. + + Returns: + batch (dict): The batch of data sampled. + ''' + if not batch_size: + batch_size = self.batch_size + + indices = np.random.randint(0, len(self), size=batch_size) + batch = {} + for k, info in self.scheme.items(): + shape = info['vshape'][1:] + v = self.__dict__[k].reshape(-1, *shape)[indices] + batch[k] = torch.as_tensor(v, device=self.device) + return batch diff --git a/safe_control_gym/safety_filters/mpsc/__init__.py b/safe_control_gym/safety_filters/mpsc/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/safe_control_gym/safety_filters/mpsc/linear_mpsc.py b/safe_control_gym/safety_filters/mpsc/linear_mpsc.py new file mode 100644 index 000000000..4f095a89e --- /dev/null +++ b/safe_control_gym/safety_filters/mpsc/linear_mpsc.py @@ -0,0 +1,362 @@ +'''Model Predictive Safety Certification (MPSC). + +The core idea is that any learning controller input can be either certificated as safe or, if not safe, corrected +using an MPC controller based on Tube MPC. + +Based on + * K.P. Wabsersich and M.N. Zeilinger 'Linear model predictive safety certification for learning-based control' 2019 + https://arxiv.org/pdf/1803.08552.pdf +''' + +import pickle +from itertools import product + +import casadi as cs +import numpy as np +from pytope import Polytope + +from safe_control_gym.controllers.lqr.lqr_utils import discretize_linear_system +from safe_control_gym.controllers.mpc.mpc_utils import rk_discrete +from safe_control_gym.envs.benchmark_env import Environment, Task +from safe_control_gym.envs.constraints import ConstrainedVariableType, LinearConstraint, QuadraticContstraint +from safe_control_gym.safety_filters.mpsc.mpsc import MPSC +from safe_control_gym.safety_filters.mpsc.mpsc_utils import (Cost_Function, compute_RPI_set, + ellipse_bounding_box, pontryagin_difference_AABB) + + +class LINEAR_MPSC(MPSC): + '''Model Predictive Safety Certification Class.''' + + def __init__(self, + env_func, + horizon: int = 10, + q_lin: list = None, + r_lin: list = None, + integration_algo: str = 'rk4', + n_samples: int = 600, + n_samples_terminal_set: int = 100, + tau: float = 0.95, + warmstart: bool = True, + additional_constraints: list = None, + use_terminal_set: bool = True, + learn_terminal_set: bool = False, + cost_function: str = Cost_Function.ONE_STEP_COST, + **kwargs + ): + '''Initialize the MPSC. + + Args: + env_func (partial gym.Env): Environment for the task. + horizon (int): The MPC horizon. + q_lin, r_lin (list): Q and R gain matrices for linear controller. + integration_algo (str): The algorithm used for integrating the dynamics, + either 'LTI', 'rk4', 'rk', or 'cvodes'. + n_samples (int): Number of samples to create W set. + n_samples_terminal_set (int): Number of samples to grow the terminal set. + tau (float): The constant use in eqn. 8.b. of the paper when finding the RPI. + warmstart (bool): If the previous MPC soln should be used to warmstart the next mpc step. + additional_constraints (list): List of additional constraints to consider. + use_terminal_set (bool): Whether to use a terminal set constraint or not. + learn_terminal_set (bool): Whether to learn a terminal set or not. + cost_function (str): A string (from Cost_Function) representing the cost function to be used. + ''' + + # Store all params/args. + for key, value in locals().items(): + if key != 'self' and key != 'kwargs' and '__' not in key: + self.__dict__[key] = value + + super().__init__(env_func, horizon, q_lin, r_lin, integration_algo, warmstart, additional_constraints, use_terminal_set, cost_function, **kwargs) + + self.terminal_set_verts = None + + def set_dynamics(self): + '''Compute the linear dynamics.''' + + # Original version, used in shooting. + dfdxdfdu = self.model.df_func(x=self.X_EQ, u=self.U_EQ) + dfdx = dfdxdfdu['dfdx'].toarray() + dfdu = dfdxdfdu['dfdu'].toarray() + delta_x = cs.MX.sym('delta_x', self.model.nx, 1) + delta_u = cs.MX.sym('delta_u', self.model.nu, 1) + self.discrete_dfdx, self.discrete_dfdu = discretize_linear_system(dfdx, dfdu, self.dt) + + if self.integration_algo == 'LTI': + x_dot_lin_vec = self.discrete_dfdx @ delta_x + self.discrete_dfdu @ delta_u + dynamics_func = cs.Function('fd', + [delta_x, delta_u], + [x_dot_lin_vec], + ['x0', 'p'], + ['xf']) + elif self.integration_algo == 'rk4': + dynamics_func = rk_discrete(self.model.fc_func, + self.model.nx, + self.model.nu, + self.dt) + else: + x_dot_lin_vec = dfdx @ delta_x + dfdu @ delta_u + dynamics_func = cs.integrator( + 'linear_discrete_dynamics', self.integration_algo, + { + 'x': delta_x, + 'p': delta_u, + 'ode': x_dot_lin_vec + }, {'tf': self.dt} + ) + + self.dynamics_func = dynamics_func + + def learn(self, + env=None + ): + '''Compute the Robust Positively Invariant (RPI) set that the MPSC used. + + Args: + env (BenchmarkEnv): If a different environment is to be used for learning, can supply it here. + ''' + + if env is None: + env = self.training_env + # Create set of error residuals. + w = np.zeros((self.model.nx, self.n_samples)) + # Use uniform sampling of control inputs and states. + for i in range(self.n_samples): + init_state, _ = env.reset() + if self.env.NAME == Environment.QUADROTOR: + u = np.random.rand(self.model.nu) / 20 - 1 / 40 + self.U_EQ + else: + u = env.action_space.sample() # Will yield a random action within action space. + x_next_obs, _, _, _ = env.step(u) + x_next_linear = np.squeeze(self.dynamics_func(x0=init_state - self.X_EQ, p=u - self.U_EQ)['xf'].toarray()) + self.X_EQ + w[:, i] = x_next_obs - x_next_linear + A_cl = self.discrete_dfdx + self.discrete_dfdu @ self.lqr_gain + self.P = compute_RPI_set(A_cl, w, self.tau) + self.omega_AABB_verts = ellipse_bounding_box(self.P) + self.tighten_state_and_input_constraints() + self.omega_constraint = QuadraticContstraint(self.env, + self.P, + 1.0, + constrained_variable=ConstrainedVariableType.STATE) + # Now that constraints are defined, setup the optimizer. + self.setup_optimizer() + + if self.learn_terminal_set: + if self.additional_constraints is not None: + print('[WARNING] Terminal set calculation assumes convex constraints') + + if self.env.TASK == Task.TRAJ_TRACKING: + self.terminal_set = Polytope(self.env.X_GOAL.T) + self.terminal_set.minimize_V_rep() + elif self.env.TASK == Task.STABILIZATION: + self.terminal_set = None + + points = None + for i in range(self.n_samples_terminal_set): + if self.terminal_set is None: + init_state = self.X_EQ + else: + init_state = self.terminal_set.V[np.random.choice(self.terminal_set.V.shape[0], 1)] + + init_state = init_state.reshape((self.model.nx, 1)) + init_state += (np.random.rand(self.model.nx, 1) - np.ones((self.model.nx, 1)) / 2) / 2 + + if self.env.NAME == Environment.QUADROTOR: + u = np.random.rand(self.model.nu) / 6 - 1 / 12 + self.U_EQ + else: + u = env.action_space.sample() # Will yield a random action within action space. + + _, feasible = self.solve_optimization(obs=init_state, uncertified_action=u) + if feasible: + if self.terminal_set is None: + self.terminal_set = Polytope(self.z_prev.T) + else: + points = np.vstack((self.z_prev.T, self.terminal_set.V)) + self.terminal_set = Polytope(points) + self.terminal_set.minimize_V_rep() + self.setup_optimizer() + + self.terminal_set_verts = points + + def load(self, + path, + ): + '''Load values used by the MPC. + + Args: + path (str): Path to the file containing the P matrix and terminal set. + ''' + + with open(path, 'rb') as file: + parameters = pickle.load(file) + + self.P = parameters['P'] + self.omega_AABB_verts = ellipse_bounding_box(self.P) + self.tighten_state_and_input_constraints() + self.omega_constraint = QuadraticContstraint(self.env, + self.P, + 1.0, + constrained_variable=ConstrainedVariableType.STATE) + + if self.learn_terminal_set and 'terminal_set' in parameters: + self.terminal_set_verts = parameters['terminal_set'] + self.terminal_set = Polytope(self.terminal_set_verts) + + # Now that constraints are defined, setup the optimizer. + self.setup_optimizer() + + def save(self, + path, + ): + '''Saves the values used by the MPC. + + Args: + path (str): Path to where to save the P matrix and terminal set. + ''' + + parameters = {} + parameters['P'] = self.P + if self.learn_terminal_set and self.terminal_set_verts is not None: + parameters['terminal_set'] = self.terminal_set_verts + + with open(path, 'wb') as file: + pickle.dump(parameters, file) + + def tighten_state_and_input_constraints(self): + '''Tigthen the state and input constraints based on the RPI.''' + + K_omega_AABB_verts_raw = (self.lqr_gain @ self.omega_AABB_verts.T).T + # Take the outermost values. + K_omega_AABB_verts_raw_limits = np.array([np.amax(K_omega_AABB_verts_raw, axis=0), np.amin(K_omega_AABB_verts_raw, axis=0)]) + self.K_omega_AABB_verts = np.vstack(list(product(*(K_omega_AABB_verts_raw_limits.T)))) + # Get the current input constraint vertices. + input_constraint = self.constraints.input_constraints + if len(input_constraint) > 1: + raise NotImplementedError('MPSC currently can\'t handle more than 1 constraint') + + input_constraint = input_constraint[0] + if self.training_env.NAME != Environment.QUADROTOR: + U_vertices_raw = [(input_constraint.upper_bounds[i], input_constraint.lower_bounds[i]) for i in range(self.model.nu)] + else: + U_vertices_raw = [(input_constraint.upper_bounds[i], -input_constraint.upper_bounds[i]) for i in range(self.model.nu)] + self.U_vertices = np.clip(np.vstack(list(product(*U_vertices_raw))), -100, 100) + self.tightened_input_constraint_verts, tightened_input_constr_func\ + = pontryagin_difference_AABB( + self.U_vertices, + self.K_omega_AABB_verts) + + if self.training_env.NAME == Environment.QUADROTOR: + min_input = input_constraint.lower_bounds[0] + np.max(self.U_vertices) - np.max(self.tightened_input_constraint_verts) + self.tightened_input_constraint_verts = np.clip(self.tightened_input_constraint_verts, min_input, 100) + self.tightened_input_constraint = tightened_input_constr_func(env=self.env, + constrained_variable=ConstrainedVariableType.INPUT) + # Get the state constraint vertices. + state_constraints = self.constraints.state_constraints + if len(state_constraints) > 1: + raise NotImplementedError('MPSC currently can\'t handle more than 1 constraint') + state_constraints = state_constraints[0] + X_vertices_raw = [(state_constraints.upper_bounds[i], state_constraints.lower_bounds[i]) for i in range(self.model.nx)] + self.X_vertices = np.clip(np.vstack(list(product(*X_vertices_raw))), -100, 100) + self.tightened_state_constraint_verts, tightened_state_constraint_func = pontryagin_difference_AABB(self.X_vertices, + self.omega_AABB_verts) + self.tightened_state_constraint = tightened_state_constraint_func(env=self.env, + constrained_variable=ConstrainedVariableType.STATE) + + self.simple_terminal_set = QuadraticContstraint(env=self.env, + P=np.eye(self.model.nx), + b=self.env.TASK_INFO['stabilization_goal_tolerance'], + constrained_variable=ConstrainedVariableType.STATE) + + def setup_optimizer(self): + '''Setup the certifying MPC problem.''' + + # Horizon parameter. + horizon = self.horizon + nx, nu = self.model.nx, self.model.nu + # Define optimizer and variables. + opti = cs.Opti() + # States. + z_var = opti.variable(nx, horizon + 1) + # Inputs. + v_var = opti.variable(nu, horizon) + # Certified input. + next_u = opti.variable(nu, 1) + # Desired input. + u_L = opti.parameter(nu, 1) + # Current observed state. + x_init = opti.parameter(nx, 1) + # Linearization point + X_EQ = opti.parameter(nx, 1) + # Reference trajectory and predicted LQR gains + if self.env.TASK == Task.STABILIZATION: + X_GOAL = opti.parameter(1, nx) + elif self.env.TASK == Task.TRAJ_TRACKING: + X_GOAL = opti.parameter(self.horizon, nx) + + # Constraints (currently only handles a single constraint for state and input). + state_constraints = self.tightened_state_constraint.get_symbolic_model() + input_constraints = self.tightened_input_constraint.get_symbolic_model() + omega_constraint = self.omega_constraint.get_symbolic_model() + simple_terminal_constraint = self.simple_terminal_set.get_symbolic_model() + for i in range(self.horizon): + # Dynamics constraints (eqn 5.b). + next_state = self.dynamics_func(x0=z_var[:, i], p=v_var[:, i])['xf'] + opti.subject_to(z_var[:, i + 1] == next_state) + # Input constraints (eqn 5.c). + opti.subject_to(input_constraints(v_var[:, i] + self.U_EQ) <= 0) + # State Constraints + opti.subject_to(state_constraints(z_var[:, i] + X_EQ) <= 0) + + # Final state constraints (5.d). + if self.use_terminal_set: + if self.terminal_set is not None: + terminal_constraint = LinearConstraint(env=self.env, A=self.terminal_set.A, b=self.terminal_set.b, constrained_variable=ConstrainedVariableType.STATE) + terminal_constraint = terminal_constraint.get_symbolic_model() + opti.subject_to(terminal_constraint(z_var[:, -1]) <= 0) + else: + opti.subject_to(simple_terminal_constraint(z_var[:, -1]) <= 0) + + # Initial state constraints (5.e). + opti.subject_to(omega_constraint(x_init - z_var[:, 0]) <= 0) + # Real input (5.f). + opti.subject_to(next_u == (v_var[:, 0] + self.U_EQ) + self.lqr_gain @ (x_init - z_var[:, 0])) + + # Create solver (IPOPT solver as of this version). + opts = {'expand': False, + 'ipopt.print_level': 4, + 'ipopt.sb': 'yes', + 'ipopt.max_iter': 50, + 'print_time': 1} + if self.integration_algo in ['LTI', 'rk4']: + opts['expand'] = True + opti.solver('ipopt', opts) + self.opti_dict = { + 'opti': opti, + 'z_var': z_var, + 'v_var': v_var, + 'next_u': next_u, + 'u_L': u_L, + 'x_init': x_init, + 'X_EQ': X_EQ, + 'X_GOAL': X_GOAL, + } + + # Cost (# eqn 5.a, note: using 2norm or sqrt makes this infeasible). + cost = self.cost_function.get_cost(self.opti_dict) + opti.minimize(cost) + + def before_optimization(self, obs): + '''Setup the optimization. + + Args: + obs (ndarray): Current state/observation. + ''' + + if self.env.NAME == Environment.CARTPOLE: + self.X_EQ = np.array([obs[0], 0, 0, 0]) + elif self.env.NAME == Environment.QUADROTOR: + self.X_EQ = np.array([obs[0], 0, obs[2], 0, 0, 0]) + + opti_dict = self.opti_dict + opti = opti_dict['opti'] + X_EQ = opti_dict['X_EQ'] + opti.set_value(X_EQ, self.X_EQ) diff --git a/safe_control_gym/safety_filters/mpsc/mpsc.py b/safe_control_gym/safety_filters/mpsc/mpsc.py new file mode 100644 index 000000000..34307fe8e --- /dev/null +++ b/safe_control_gym/safety_filters/mpsc/mpsc.py @@ -0,0 +1,258 @@ +'''Abstract Model Predictive Safety Certification (MPSC). + +The core idea is that any learning controller input can be either certificated as safe or, if not safe, corrected +using an MPC controller based on Tube MPC. + +Based on + * K.P. Wabsersich and M.N. Zeilinger 'Linear model predictive safety certification for learning-based control' 2019 + https://arxiv.org/pdf/1803.08552.pdf +''' + +from abc import ABC, abstractmethod +from copy import deepcopy + +import numpy as np + +from safe_control_gym.controllers.lqr.lqr_utils import compute_lqr_gain +from safe_control_gym.controllers.mpc.mpc_utils import get_cost_weight_matrix, reset_constraints +from safe_control_gym.safety_filters.base_safety_filter import BaseSafetyFilter +from safe_control_gym.safety_filters.mpsc.mpsc_cost_function.one_step_cost import ONE_STEP_COST +from safe_control_gym.safety_filters.mpsc.mpsc_utils import Cost_Function, get_trajectory_on_horizon + + +class MPSC(BaseSafetyFilter, ABC): + '''Abstract Model Predictive Safety Certification Class.''' + + def __init__(self, + env_func, + horizon: int = 10, + q_lin: list = None, + r_lin: list = None, + integration_algo: str = 'rk4', + warmstart: bool = True, + additional_constraints: list = None, + use_terminal_set: bool = True, + cost_function: Cost_Function = Cost_Function.ONE_STEP_COST, + **kwargs + ): + '''Initialize the MPSC. + + Args: + env_func (partial BenchmarkEnv): Environment for the task. + horizon (int): The MPC horizon. + q_lin, r_lin (list): Q and R gain matrices for linear controller. + integration_algo (str): The algorithm used for integrating the dynamics, + either 'LTI', 'rk4', 'rk', or 'cvodes'. + warmstart (bool): If the previous MPC soln should be used to warmstart the next mpc step. + additional_constraints (list): List of additional constraints to consider. + use_terminal_set (bool): Whether to use a terminal set constraint or not. + cost_function (Cost_Function): A string (from Cost_Function) representing the cost function to be used. + ''' + + # Store all params/args. + for k, v in locals().items(): + if k != 'self' and k != 'kwargs' and '__' not in k: + self.__dict__[k] = v + + super().__init__(env_func, **kwargs) + + np.random.seed(self.seed) + + # Setup the Environments. + self.env = env_func(normalized_rl_action_space=False) + self.training_env = env_func(randomized_init=True, + init_state=None, + cost='quadratic', + normalized_rl_action_space=False, + ) + + # Setup attributes. + self.reset() + self.dt = self.model.dt + self.Q = get_cost_weight_matrix(q_lin, self.model.nx) + self.R = get_cost_weight_matrix(r_lin, self.model.nu) + + self.X_EQ = np.zeros(self.model.nx) + self.U_EQ = self.model.U_EQ + + self.set_dynamics() + self.lqr_gain = -compute_lqr_gain(self.model, self.X_EQ, self.U_EQ, self.Q, self.R, discrete_dynamics=True) + + self.terminal_set = None + + if self.additional_constraints is None: + additional_constraints = [] + self.constraints, self.state_constraints_sym, self.input_constraints_sym = reset_constraints( + self.env.constraints.constraints + additional_constraints) + + if cost_function == Cost_Function.ONE_STEP_COST: + self.cost_function = ONE_STEP_COST() + else: + raise NotImplementedError(f'The MPSC cost function {cost_function} has not been implemented') + + @abstractmethod + def set_dynamics(self): + '''Compute the dynamics.''' + raise NotImplementedError + + @abstractmethod + def setup_optimizer(self): + '''Setup the certifying MPC problem.''' + raise NotImplementedError + + def before_optimization(self, obs): + '''Setup the optimization. + + Args: + obs (ndarray): Current state/observation. + ''' + return + + def solve_optimization(self, + obs, + uncertified_action, + iteration=None, + ): + '''Solve the MPC optimization problem for a given observation and uncertified input. + + Args: + obs (ndarray): Current state/observation. + uncertified_action (ndarray): The uncertified_controller's action. + iteration (int): The current iteration, used for trajectory tracking. + + Returns: + action (ndarray): The certified action. + feasible (bool): Whether the safety filtering was feasible or not. + ''' + + opti_dict = self.opti_dict + opti = opti_dict['opti'] + z_var = opti_dict['z_var'] + v_var = opti_dict['v_var'] + next_u = opti_dict['next_u'] + u_L = opti_dict['u_L'] + x_init = opti_dict['x_init'] + X_GOAL = opti_dict['X_GOAL'] + opti.set_value(x_init, obs - self.X_EQ) + opti.set_value(u_L, uncertified_action) + clipped_X_GOAL = get_trajectory_on_horizon(self.env, iteration, self.horizon) + opti.set_value(X_GOAL, clipped_X_GOAL) + + self.cost_function.prepare_cost_variables(opti_dict, obs, iteration) + + # Initial guess for optimization problem. + if (self.warmstart and self.z_prev is not None and self.v_prev is not None): + # Shift previous solutions by 1 step. + z_guess = deepcopy(self.z_prev) + v_guess = deepcopy(self.v_prev) + z_guess[:, :-1] = z_guess[:, 1:] + v_guess[:-1] = v_guess[1:] + opti.set_initial(z_var, z_guess) + opti.set_initial(v_var, v_guess) + + # Solve the optimization problem. + try: + sol = opti.solve() + x_val, u_val, next_u_val = sol.value(z_var), sol.value(v_var), sol.value(next_u) + self.z_prev = x_val + self.v_prev = u_val.reshape((self.model.nu), self.horizon) + self.next_u_prev = next_u_val + # Take the first one from solved action sequence. + action = next_u_val + self.prev_action = next_u_val + feasible = True + except Exception as e: + print('Error Return Status:', opti.debug.return_status()) + print(e) + feasible = False + action = None + return action, feasible + + def certify_action(self, + current_state, + uncertified_action, + info=None, + ): + '''Algorithm 1 from Wabsersich 2019. + + Args: + current_state (ndarray): Current state/observation. + uncertified_action (ndarray): The uncertified_controller's action. + info (dict): The info at this timestep. + + Returns: + certified_action (ndarray): The certified action + success (bool): Whether the safety filtering was successful or not. + ''' + uncertified_action = np.clip(uncertified_action, self.env.physical_action_bounds[0], self.env.physical_action_bounds[1]) + self.results_dict['uncertified_action'].append(uncertified_action) + success = True + + self.before_optimization(current_state) + iteration = self.extract_step(info) + action, feasible = self.solve_optimization(current_state, uncertified_action, iteration) + self.results_dict['feasible'].append(feasible) + + if feasible: + self.kinf = 0 + certified_action = action + else: + self.kinf += 1 + if (self.kinf <= self.horizon - 1 and self.z_prev is not None and self.v_prev is not None): + action = np.squeeze(self.v_prev[:, self.kinf]) + \ + np.squeeze(self.lqr_gain @ (current_state.reshape((self.model.nx, 1)) - self.z_prev[:, self.kinf].reshape((self.model.nx, 1)))) + if self.integration_algo == 'LTI': + action = np.squeeze(action) + np.squeeze(self.U_EQ) + action = np.squeeze(action) + clipped_action = np.clip(action, self.constraints.input_constraints[0].lower_bounds, self.constraints.input_constraints[0].upper_bounds) + + if np.linalg.norm(clipped_action - action) >= 0.01: + success = False + certified_action = clipped_action + else: + action = np.squeeze(self.lqr_gain @ (current_state - self.X_EQ)) + if self.integration_algo == 'LTI': + action += np.squeeze(self.U_EQ) + clipped_action = np.clip(action, self.constraints.input_constraints[0].lower_bounds, self.constraints.input_constraints[0].upper_bounds) + + success = False + certified_action = clipped_action + + certified_action = np.squeeze(np.array(certified_action)) + self.results_dict['kinf'].append(self.kinf) + self.results_dict['certified_action'].append(certified_action) + self.results_dict['correction'].append(np.linalg.norm(certified_action - uncertified_action)) + + return certified_action, success + + def setup_results_dict(self): + '''Setup the results dictionary to store run information.''' + self.results_dict = {} + self.results_dict['feasible'] = [] + self.results_dict['kinf'] = [] + self.results_dict['uncertified_action'] = [] + self.results_dict['certified_action'] = [] + self.results_dict['correction'] = [] + + def close(self): + '''Cleans up resources.''' + self.env.close() + self.training_env.close() + + def reset(self): + '''Prepares for training or evaluation.''' + self.model = self.get_prior(self.env, self.prior_info) + self.env.reset() + self.training_env.reset() + self.reset_before_run() + + def reset_before_run(self, env=None): + '''Reinitialize just the controller before a new run. + + Args: + env (BenchmarkEnv): The environment to be used for the new run. + ''' + self.z_prev = None + self.v_prev = None + self.kinf = self.horizon - 1 + self.setup_results_dict() diff --git a/safe_control_gym/safety_filters/mpsc/mpsc.yaml b/safe_control_gym/safety_filters/mpsc/mpsc.yaml new file mode 100644 index 000000000..9d2798d0c --- /dev/null +++ b/safe_control_gym/safety_filters/mpsc/mpsc.yaml @@ -0,0 +1,28 @@ +# LQR controller parameters +r_lin: + - 1. +q_lin: + - 1. + +# MPC Parameters +horizon: 20 +warmstart: False +integration_algo: rk4 +use_terminal_set: False + +# Prior info +prior_info: + prior_prop: null + randomize_prior_prop: False + prior_prop_rand_info: null + +# Safe set calculation +n_samples: 600 +n_samples_terminal_set: 10 +learn_terminal_set: False + +# Tau parameter for the calcuation of the RPI +tau: 0.95 + +# Cost function +cost_function: one_step_cost diff --git a/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/abstract_cost.py b/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/abstract_cost.py new file mode 100644 index 000000000..3980790c2 --- /dev/null +++ b/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/abstract_cost.py @@ -0,0 +1,45 @@ +'''Abstract class for MPSC Cost Functions.''' + +from abc import ABC, abstractmethod + +from safe_control_gym.envs.benchmark_env import BenchmarkEnv + + +class MPSC_COST(ABC): + '''Abstract MPSC Cost Function to be extended for different cost functions.''' + + def __init__(self, + env: BenchmarkEnv = None, + ): + '''Initialize the MPSC Cost. + + Args: + env (BenchmarkEnv): Environment for the task. + ''' + + self.env = env + + # Setup attributes. + self.model = self.env.symbolic if env is not None else None + + @abstractmethod + def get_cost(self, opti_dict): + '''Returns the cost function for the MPSC optimization in symbolic form. + + Args: + opti_dict (dict): The dictionary of optimization variables. + + Returns: + cost (casadi symbolic expression): The symbolic cost function using casadi. + ''' + raise NotImplementedError + + def prepare_cost_variables(self, opti_dict, obs, iteration): + '''Prepares all the symbolic variable initial values for the next optimization. + + Args: + opti_dict (dict): The dictionary of optimization variables. + obs (ndarray): Current state/observation. + iteration (int): The current iteration, used for trajectory tracking. + ''' + return diff --git a/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/one_step_cost.py b/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/one_step_cost.py new file mode 100644 index 000000000..d0b3799bc --- /dev/null +++ b/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/one_step_cost.py @@ -0,0 +1,23 @@ +'''Standard cost function for MPSC, minimizing change in the next step's input.''' + +from safe_control_gym.safety_filters.mpsc.mpsc_cost_function.abstract_cost import MPSC_COST + + +class ONE_STEP_COST(MPSC_COST): + '''Standard one step MPSC Cost Function.''' + + def get_cost(self, opti_dict): + '''Returns the cost function for the MPSC optimization in symbolic form. + + Args: + opti_dict (dict): The dictionary of optimization variables. + + Returns: + cost (casadi symbolic expression): The symbolic cost function using casadi. + ''' + + next_u = opti_dict['next_u'] + u_L = opti_dict['u_L'] + + cost = (u_L - next_u).T @ (u_L - next_u) + return cost diff --git a/safe_control_gym/safety_filters/mpsc/mpsc_utils.py b/safe_control_gym/safety_filters/mpsc/mpsc_utils.py new file mode 100644 index 000000000..89540d6ca --- /dev/null +++ b/safe_control_gym/safety_filters/mpsc/mpsc_utils.py @@ -0,0 +1,144 @@ +'''Utility functions for Model Predictive Safety Certification.''' + +from enum import Enum +from functools import partial +from itertools import product + +import cvxpy as cp +import numpy as np +import pytope as pt + +from safe_control_gym.envs.benchmark_env import Task +from safe_control_gym.envs.constraints import BoundedConstraint, LinearConstraint + + +class Cost_Function(str, Enum): + '''MPSC Cost functions enumeration class.''' + + ONE_STEP_COST = 'one_step_cost' # Default MPSC cost function. + + +def compute_RPI_set(Acl, + w, + tau + ): + '''Compute a Robust positively invariant set (RPI). + + This follows the method in sec IV A from [1] to compute the robust positively invartiant set through a convex + optimzation problem (LMI?) (equation 8a and 8b). + + [1] K.P. Wabsersich and M.N. Zeilinger 'Linear model predictive safety certification for learning-based control' + 2019. https://arxiv.org/pdf/1803.08552.pdf + + Args: + Acl (ndarray): Closed loop gain matrix A+BK (nx by nx) + w (ndarray): Collection of dynamics error residuals with dim (nx by n_samples) + tau (float): Coefficient from the s-procedure. Somewhere near 0.9 seems to work. + + Returns: + P (ndarray): P from eqn 8 that defines the ellipsoidal RPI set. + ''' + + n_samples = w.shape[1] + P = cp.Variable(Acl.shape, PSD=True) + constraints = [] + for i in range(n_samples): + w_i = w[:, i, None] + con_11 = Acl.T @ P @ Acl - tau * P + con_12 = Acl.T @ P @ w_i + con_21 = w_i.T @ P @ Acl + con_22 = w_i.T @ P @ w_i + tau - 1 + constraints += [cp.bmat([[con_11, con_12], + [con_21, con_22]]) << 0] + prob = cp.Problem(cp.Minimize(-cp.log_det(P)), constraints) + try: + prob.solve(solver=cp.MOSEK, verbose=True) + if 'optimal' not in prob.status: + raise cp.SolverError + except cp.SolverError: + print('[ERROR] RPI Computation failed. Ensure you have the MOSEK solver. Otherwise, error unknown.') + exit() + return P.value + + +def ellipse_bounding_box(P): + '''Finds the bounding box of an ellipse defined by x^T P x <= 1. + + Args: + P (ndarray): n by n array defining the ellipse. + + Returns: + vertices (ndarray): A vertical of the vertices (number of verts by dim of space). + ''' + + c = np.eye(P.shape[0]) + extremes = [] + for i in range(P.shape[0]): + extremes.append((np.sqrt(c[:, i, None].T @ np.linalg.inv(P) @ c[:, i, None])[0, 0], + -np.sqrt(c[:, i, None].T @ np.linalg.inv(P) @ c[:, i, None])[0, 0])) + vertices = list(product(*extremes)) + return np.vstack(vertices) + + +def pontryagin_difference_AABB(verts1, + verts2 + ): + '''Computre verts1 (-) verts2. + + Args: + verts1, verts2 (ndarray): Array of vertices ( n vertices by space dimension) stacked vertically + so that every vertix is a horizonal array: + vertsi = [v1, + v2, + ..., + vn] + Returns: + verts (ndarray): Array of pontyagin difference. + const_func (partial constraint): Constraint function initialized with parameters from the difference. + ''' + + if verts1.shape[1] > 1: + # If greater than 2 dimensions. + poly1 = pt.Polytope(verts1) + poly2 = pt.Polytope(verts2) + diff = poly1 - poly2 + A = diff.A + b = diff.b + verts = diff.V + const_func = partial(LinearConstraint, A=A, b=b) + return verts, const_func + else: + # If 1D data. Only handles closed compact sets. + vert2_range = np.ptp(verts2) + vert_min = np.min(verts1) + vert2_range / 2 + vert_max = np.max(verts1) - vert2_range / 2 + const_func = partial(BoundedConstraint, lower_bounds=vert_min, upper_bounds=vert_max) + if vert_max > vert_min: + return np.vstack((vert_min, vert_max)), const_func + else: + print('Warning: Tightend set is the Zero set.') + return np.array([[0, 0]]).T, const_func + + +def get_trajectory_on_horizon(env, iteration, horizon): + '''Gets the trajectory segment for the next horizon steps. + + Args: + env (BenchmarkEnv): Environment for the task. + iteration (int): The current iteration, used for trajectory tracking. + horizon (int): The MPC horizon. + + Returns: + clipped_X_GOAL (ndarray): The trajectory for the next horizon steps. + ''' + + if env.TASK == Task.TRAJ_TRACKING: + wp_idx = [ + min(iteration + i, env.X_GOAL.shape[0] - 1) + for i in range(horizon) + ] + clipped_X_GOAL = env.X_GOAL[wp_idx] + else: + clipped_X_GOAL = env.X_GOAL + + return clipped_X_GOAL diff --git a/safe_control_gym/utils/logging.py b/safe_control_gym/utils/logging.py index be73ab426..5b43d41df 100644 --- a/safe_control_gym/utils/logging.py +++ b/safe_control_gym/utils/logging.py @@ -1,25 +1,22 @@ -"""Logging utilities. +'''Logging utilities.''' -""" -from collections import defaultdict import logging import os -import numpy as np -import imageio +from collections import defaultdict +import imageio +import numpy as np from torch.utils.tensorboard import SummaryWriter class StdoutLogger: - """Channel print content to std out and log file. - - """ + '''Channel print content to std out and log file.''' def __init__(self, logger_name, log_dir, level=logging.INFO): logger = logging.getLogger(logger_name) formatter = logging.Formatter('%(asctime)s : %(message)s') # Log to file ('w' to overwrite, 'a' to keep appending). - log_file = os.path.join(log_dir, "std_out.txt") + log_file = os.path.join(log_dir, 'std_out.txt') file_handler = logging.FileHandler(log_file, mode='a') file_handler.setFormatter(formatter) # Log to std out. @@ -32,39 +29,32 @@ def __init__(self, logger_name, log_dir, level=logging.INFO): self.file_handler = file_handler def info(self, msg): - """Chain print message to logger. - - """ + '''Chain print message to logger.''' self.logger.info(msg) def close(self): - """Free log file. - - """ + '''Free log file.''' self.file_handler.close() class FileLogger: - """Logger for saving statistics and other outputs to text files. - + '''Logger for saving statistics and other outputs to text files. + Based on https://github.com/michaelrzhang/logger - + Initializes the log directory and creates log files given by name in arguments. Can be used to append future log values to each file. - - """ + ''' def __init__(self, log_dir): # Creates folder for logging stats - self.log_dir = os.path.join(log_dir, "logs") + self.log_dir = os.path.join(log_dir, 'logs') os.makedirs(self.log_dir, exist_ok=True) # name of stats being tracked self.log_names = [] - def init_logfile(self, name, xlabel="step"): - """Makes text file for logging the stat. - - """ + def init_logfile(self, name, xlabel='step'): + '''Makes text file for logging the stat.''' fname = self.get_log_fname(name) # Already exist due to restore. if os.path.exists(fname): @@ -72,38 +62,32 @@ def init_logfile(self, name, xlabel="step"): dir_name = os.path.dirname(os.path.realpath(fname)) os.makedirs(dir_name, exist_ok=True) with open(fname, 'w') as log_file: - log_file.write("{},{}\n".format(xlabel, name)) + log_file.write(f'{xlabel},{name}\n') def get_log_fname(self, name): - """Gets log file name for the stat. - - """ - return os.path.join(self.log_dir, '{}.log'.format(name)) + '''Gets log file name for the stat.''' + return os.path.join(self.log_dir, f'{name}.log') def log(self, name, value, step): - """Logs the stat to its corresponding text file. - - """ + '''Logs the stat to its corresponding text file.''' if name not in self.log_names: # Initialize only if not done so already. self.init_logfile(name) self.log_names.append(name) fname = self.get_log_fname(name) with open(fname, 'a') as log_file: - log_file.write("{},{}\n".format(step, value)) + log_file.write(f'{step},{value}\n') def restore(self, step): - """Resets all log files to ignore lines after `step`. - - """ + '''Resets all log files to ignore lines after `step`.''' # Find all stats log files. log_files = [] for res, _, files in os.walk(self.log_dir): for each_file in files: - if ".log" in files: + if '.log' in files: log_files.append(os.path.join(res, each_file)) for fname in log_files: - with open(fname, "r") as file: + with open(fname, 'r') as file: lines = file.readlines() # Find which line to start purging. stop_idx = None @@ -119,15 +103,13 @@ def restore(self, step): break # Overwrite log file with only valid lines. lines = lines[:stop_idx] - with open(fname, "w") as file: + with open(fname, 'w') as file: for each_line in lines: file.write(each_line) class ExperimentLogger: - """A hybrid logger. - - """ + '''A hybrid logger.''' def __init__(self, log_dir, @@ -135,15 +117,14 @@ def __init__(self, log_file_out=False, use_tensorboard=False ): - """Initializes loggers. + '''Initializes loggers. Args: log_dir (str): name of folder to save logs. - log_std_out (bool): if to save terminal logs. - log_file_out (bool): if to write data logs to text files. + log_std_out (bool): if to save terminal logs. + log_file_out (bool): if to write data logs to text files. use_tensorboard (bool): if to use tensorboard. - - """ + ''' self.log_dir = log_dir os.makedirs(log_dir, exist_ok=True) # Container for a log period. @@ -151,7 +132,7 @@ def __init__(self, # Terminal logging. self.log_std_out = log_std_out if log_std_out: - self.std_out_logger = StdoutLogger("Benchmark", log_dir) + self.std_out_logger = StdoutLogger('Benchmark', log_dir) # Text file logging. self.log_file_out = log_file_out if log_file_out: @@ -162,9 +143,7 @@ def __init__(self, self.tb_logger = SummaryWriter(log_dir=log_dir) def load(self, step): - """Resume from experiment, but ignores any logs after `step`. - - """ + '''Resume from experiment, but ignores any logs after `step`.''' if self.log_file_out: self.file_logger.restore(step) if self.use_tensorboard: @@ -172,18 +151,14 @@ def load(self, step): purge_step=step) def close(self): - """Cleans up logging resources. - - """ + '''Cleans up logging resources.''' if self.log_std_out: self.std_out_logger.close() if self.use_tensorboard: self.tb_logger.close() def info(self, msg): - """Logs a message to std output. - - """ + '''Logs a message to std output.''' if self.log_std_out: self.std_out_logger.info(msg) else: @@ -196,9 +171,7 @@ def add_scalar(self, store=True, write=True, write_tb=True): - """Logs a scalar data. - - """ + '''Logs a scalar data.''' # Add to buffer (to be logged to terminal). if store: self.stats_buffer[name].append(val) @@ -216,74 +189,69 @@ def add_scalars(self, store=True, write=True, write_tb=True): - """Logs a group of scalars. - - """ + '''Logs a group of scalars.''' assert isinstance(data, dict) for name, val in data.items(): # Scalars under the same name group. - full_name = prefix + "/" + name if prefix else name + full_name = prefix + '/' + name if prefix else name self.add_scalar(full_name, val, step, store, write, write_tb) def dump_scalars(self): - """Produce a summary of stats within the log period (from buffer). + '''Produce a summary of stats within the log period (from buffer). - Currently only dump to terminal as a table summary, - can dump to a CSV file in the future, + Currently only dump to terminal as a table summary, + can dump to a CSV file in the future, but feels repetitive & less flexible than `add_scalar(..., write=True)`. - - """ + ''' keys, values = [], [] tag = None # Important: sorted keys are important for consistency betwen log steps. for key, val_list in sorted(self.stats_buffer.items()): if len(val_list) == 1: # Left align. - val_str = "{:<8.3g}".format(val_list[0]) + val_str = '{:<8.3g}'.format(val_list[0]) else: val_np = np.asarray(val_list) - val_str = "{:.3f} +/- {:.3f}".format(val_np.mean(), val_np.std()) + val_str = '{:.3f} +/- {:.3f}'.format(val_np.mean(), val_np.std()) # Find tag and add it to the dict. - if key.find("/") > 0: - tag = key[:key.find("/") + 1] + if key.find('/') > 0: + tag = key[:key.find('/') + 1] trunc_tag = self._truncate(tag) if trunc_tag not in keys: keys.append(trunc_tag) - values.append("") + values.append('') # Remove tag from key. if tag is not None and tag in key: - key = str(" " + key[len(tag):]) + key = str(' ' + key[len(tag):]) keys.append(self._truncate(key)) values.append(self._truncate(val_str)) # Find max widths. if len(keys) == 0: - print("Tried to write empty key-value dict") + print('Tried to write empty key-value dict') return else: key_width = max(map(len, keys)) val_width = max(map(len, values)) # Write out the data. - dashes = "-" * (key_width + val_width + 7) + dashes = '-' * (key_width + val_width + 7) lines = [dashes] for key, value in zip(keys, values): - key_space = " " * (key_width - len(key)) - val_space = " " * (val_width - len(value)) - lines.append("| {}{} | {}{} |".format(key, key_space, value, val_space)) + key_space = ' ' * (key_width - len(key)) + val_space = ' ' * (val_width - len(value)) + lines.append('| {}{} | {}{} |'.format(key, key_space, value, val_space)) lines.append(dashes) - summary = "\n" + "\n".join(lines) + "\n" + summary = '\n' + '\n'.join(lines) + '\n' self.info(summary) self.stats_buffer.clear() def _truncate(self, string, max_length=23): if len(string) > max_length: - return string[:max_length - 3] + "..." + return string[:max_length - 3] + '...' else: return string def log_video(self, name, video, fps=20): - """Saves a video for evaluation, video: list of np.arrays of shape (H,W,C). - - """ + '''Saves a video for evaluation, video: list of np.arrays of shape (H,W,C).''' vid_kargs = {'fps': fps} - vid_name = '{}/{}'.format(self.log_dir, name) + vid_name = f'{self.log_dir}/{name}' imageio.mimsave(vid_name, video, **vid_kargs) diff --git a/safe_control_gym/utils/plotting.py b/safe_control_gym/utils/plotting.py index 5895f4e41..d5eaa0f9b 100644 --- a/safe_control_gym/utils/plotting.py +++ b/safe_control_gym/utils/plotting.py @@ -1,44 +1,42 @@ -"""Plotting utilities. +'''Plotting utilities.''' -""" -from collections import defaultdict import os import os.path as osp -import numpy as np -import matplotlib.pyplot as plt import re +from collections import defaultdict +import matplotlib.pyplot as plt +import numpy as np from tensorboard.backend.event_processing.event_accumulator import EventAccumulator - DIV_LINE_WIDTH = 50 COLORS = [ - "blue", - "green", - "red", - "black", - "cyan", - "magenta", - "yellow", - "brown", - "purple", - "pink", - "orange", - "teal", - "coral", - "lightblue", - "lime", - "lavender", - "turquoise", - "darkgreen", - "tan", - "salmon", - "gold", - "lightpurple", - "darkred", - "darkblue", + 'blue', + 'green', + 'red', + 'black', + 'cyan', + 'magenta', + 'yellow', + 'brown', + 'purple', + 'pink', + 'orange', + 'teal', + 'coral', + 'lightblue', + 'lime', + 'lavender', + 'turquoise', + 'darkgreen', + 'tan', + 'salmon', + 'gold', + 'lightpurple', + 'darkred', + 'darkblue', ] @@ -60,31 +58,25 @@ ('dashdotdotted', (0, (3, 5, 1, 5, 1, 5))), ('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))), ('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1))) -] + ] def rolling_window(a, window): - """Window data. - - """ + '''Window data.''' shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) strides = a.strides + (a.strides[-1],) return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) def window_func(x, y, window, func): - """Evaluate a function on windowed data. - - """ + '''Evaluate a function on windowed data.''' yw = rolling_window(y, window) yw_func = func(yw, axis=-1) return x[window - 1:], yw_func -def filter_log_dirs(pattern, negative_pattern=" ", root="./log", **kwargs): - """Gets list of experiment folders as specified. - - """ +def filter_log_dirs(pattern, negative_pattern=' ', root='./log', **kwargs): + '''Gets list of experiment folders as specified.''' dirs = [item[0] for item in os.walk(root)] leaf_dirs = [] for i in range(len(dirs)): @@ -93,20 +85,18 @@ def filter_log_dirs(pattern, negative_pattern=" ", root="./log", **kwargs): leaf_dirs.append(dirs[i]) names = [] p = re.compile(pattern) - np = re.compile(negative_pattern) - for dir in leaf_dirs: - if p.match(dir) and not np.match(dir): - names.append(dir) - print(dir) - print("") + neg_p = re.compile(negative_pattern) + for leaf_dir in leaf_dirs: + if p.match(leaf_dir) and not neg_p.match(leaf_dir): + names.append(leaf_dir) + print(leaf_dir) + print('') return sorted(names) def align_runs(xy_list, x_num_max=None): - """Aligns the max of the x data across runs. - - """ - x_max = float("inf") + '''Aligns the max of the x data across runs.''' + x_max = float('inf') for x, y in xy_list: # Align length of x data (get min across all runs). x_max = min(x_max, len(x)) @@ -117,9 +107,7 @@ def align_runs(xy_list, x_num_max=None): def smooth_runs(xy_list, window=10): - """Smooth the data curves by mean filtering. - - """ + '''Smooth the data curves by mean filtering.''' smoothed_list = [ window_func(np.asarray(x), np.asarray(y), window, np.mean) for x, y in xy_list @@ -128,9 +116,7 @@ def smooth_runs(xy_list, window=10): def select_runs(xy_list, criterion, top_k=0): - """Pickes the top k runs based on a criterion. - - """ + '''Pickes the top k runs based on a criterion.''' perf = [criterion(y) for _, y in xy_list] top_k_runs = np.argsort(perf)[-top_k:] selected_list = [] @@ -141,10 +127,8 @@ def select_runs(xy_list, criterion, top_k=0): def interpolate_runs(xy_list, interp_interval=100): - """Uses the same x data by interpolation across runs. - - """ - x_right = float("inf") + '''Uses the same x data by interpolation across runs.''' + x_right = float('inf') for x, y in xy_list: x_right = min(x_right, x[-1]) # Shape: (data_len,). @@ -158,17 +142,15 @@ def interpolate_runs(xy_list, interp_interval=100): def load_from_log_file(path): - """Return x, y sequence data from the stat csv. - - """ - with open(path, "r") as f: + '''Return x, y sequence data from the stat csv.''' + with open(path, 'r') as f: lines = f.readlines() # Labels. - xk, yk = [k.strip() for k in lines[0].strip().split(",")] + xk, yk = [k.strip() for k in lines[0].strip().split(',')] # Values. x, y = [], [] - for l in lines[1:]: - data = l.strip().split(",") + for line in lines[1:]: + data = line.strip().split(',') x.append(float(data[0].strip())) y.append(float(data[1].strip())) x = np.array(x) @@ -177,44 +159,41 @@ def load_from_log_file(path): def load_from_logs(log_dir): - """Return dict of stats under log_dir folder (`exp_dir/logs/`). - - """ + '''Return dict of stats under log_dir folder (`exp_dir/logs/`).''' log_files = [] # Fetch all log files. for r, _, f in os.walk(log_dir): for file in f: - if ".log" in file: + if '.log' in file: log_files.append(os.path.join(r, file)) # Fetch all stats from log files. data = {} for path in log_files: - name = path.split(log_dir)[-1].replace(".log", "") + name = path.split(log_dir)[-1].replace('.log', '') xk, x, yk, y = load_from_log_file(path) data[name] = (xk, x, yk, y) return data def plot_from_logs(src_dir, out_dir, window=None, keys=None): - """Generate a plot for each stat in an experiment `logs` folder. - + '''Generate a plot for each stat in an experiment `logs` folder. + Args: src_dir (str): folder to read logs. out_dir (str): folder to save figures. window (int): window size for smoothing. keys (list): specify name of stats to plot, None means plot all. - - """ + ''' # Find all logs. log_files = [] for r, _, f in os.walk(src_dir): for file in f: - if ".log" in file: + if '.log' in file: log_files.append(os.path.join(r, file)) # Make a figure for each log file. stats = {} for path in log_files: - name = path.split(src_dir)[-1].replace(".log", "") + name = path.split(src_dir)[-1].replace('.log', '') if keys: if name not in keys: continue @@ -227,7 +206,7 @@ def plot_from_logs(src_dir, out_dir, window=None, keys=None): plt.title(name) plt.xlabel(xk) plt.ylabel(yk) - plt.savefig(os.path.join(out_dir, name.replace("/", "-") + ".jpg")) + plt.savefig(os.path.join(out_dir, name.replace('/', '-') + '.jpg')) return stats @@ -235,14 +214,12 @@ def plot_from_tensorboard_log(src_dir, out_dir, window=None, keys=None, - xlabel="step"): - """Generates a plot for each stat from tfb log file in source folder. - - """ + xlabel='step'): + '''Generates a plot for each stat from tfb log file in source folder.''' event_acc = EventAccumulator(src_dir) event_acc.Reload() if not keys: - keys = event_acc.Tags()["scalars"] + keys = event_acc.Tags()['scalars'] stats = {} for k in keys: _, x, y = zip(*event_acc.Scalars(k)) @@ -255,55 +232,54 @@ def plot_from_tensorboard_log(src_dir, plt.title(k) plt.xlabel(xlabel) plt.ylabel(k) - # Use "-" instead of "/" to connect group and stat name. - out_path = os.path.join(out_dir, k.replace("/", "-") + ".jpg") + # Use '-' instead of '/' to connect group and stat name. + out_path = os.path.join(out_dir, k.replace('/', '-') + '.jpg') plt.savefig(out_path) return stats def plot_from_experiments(legend_dir_specs, - out_path="temp.jpg", + out_path='temp.jpg', scalar_name=None, - title="Traing Curves", - xlabel="Epochs", - ylabel="Loss", + title='Traing Curves', + xlabel='Epochs', + ylabel='Loss', window=None, x_num_max=None, num_std=1, use_tb_log=True ): - """Generates plot among algos, each with several seed runs. - - Example: - make a plot on average reward for gnn and mlp:: - + '''Generates plot among algos, each with several seed runs. + + Example: + make a plot on average reward for gnn and mlp: + > plot_from_experiments( { - "gnn": [ - "results/algo1/seed0", - "results/algo1/seed1", - "results/algo1/seed2" + 'gnn': [ + 'results/algo1/seed0', + 'results/algo1/seed1', + 'results/algo1/seed2' ], - "mlp": [ - "results/algo2/seed6", - "results/algo2/seed1", - "results/algo2/seed9", - "results/algo2/seed3" + 'mlp': [ + 'results/algo2/seed6', + 'results/algo2/seed1', + 'results/algo2/seed9', + 'results/algo2/seed3' ], }, - out_path="avg_reward.jpg", - scalar_name="loss_eval/total_rewards", - title="Average Reward", - xlabel="Epochs", - ylabel="Reward", + out_path='avg_reward.jpg', + scalar_name='loss_eval/total_rewards', + title='Average Reward', + xlabel='Epochs', + ylabel='Reward', window=10 ) - - """ - assert scalar_name is not None, "Must provide a scalar name to plot" + ''' + assert scalar_name is not None, 'Must provide a scalar name to plot' # Get all stats. stats = defaultdict(list) - for l, dirs in legend_dir_specs.items(): + for stat, dirs in legend_dir_specs.items(): for d in dirs: # Pick from either log source (tensorboard or log text files). if use_tb_log: @@ -312,15 +288,15 @@ def plot_from_experiments(legend_dir_specs, _, x, y = zip(*event_acc.Scalars(scalar_name)) del event_acc else: - path = os.path.join(d, "logs", scalar_name + ".log") + path = os.path.join(d, 'logs', scalar_name + '.log') _, x, _, y = load_from_log_file(path) # Smoothing. x, y = np.asarray(x), np.asarray(y) if window: x, y = window_func(x, y, window, np.mean) - stats[l].append([x, y]) + stats[stat].append([x, y]) # Post-processing. - x_max = float("inf") + x_max = float('inf') for _, runs in stats.items(): for x, y in runs: # Align length of x data (get min across all runs & all algos). @@ -361,24 +337,26 @@ def get_log_dirs(all_logdirs, select=None, exclude=None ): - """Find all folders for plotting. + '''Find all folders for plotting. All 3 arguments can be exposed as list args from command line. For every entry in all_logdirs, - 1) check if the entry is a real directory and if it is, - pull data from it; - 2) if not, check to see if the entry is a prefix for a + 1) check if the entry is a real directory and if it is, + pull data from it; + 2) if not, check to see if the entry is a prefix for a real directory, and pull data from that. - - """ + ''' logdirs = [] for logdir in all_logdirs: if osp.isdir(logdir) and logdir[-1] == os.sep: logdirs += [logdir] else: basedir = osp.dirname(logdir) - fulldir = lambda x: osp.join(basedir, x) + + def fulldir(x): + return osp.join(basedir, x) + prefix = logdir.split(os.sep)[-1] listdir = os.listdir(basedir) logdirs += sorted([fulldir(x) for x in listdir if prefix in x]) diff --git a/safe_control_gym/utils/registration.py b/safe_control_gym/utils/registration.py index dfbbacdfc..8adb3bb5e 100644 --- a/safe_control_gym/utils/registration.py +++ b/safe_control_gym/utils/registration.py @@ -1,77 +1,67 @@ -"""Environments and controllers registration. +'''Environments and controllers registration. Based on https://github.com/openai/gym/blob/master/gym/envs/registration.py +''' -""" import copy -import yaml import importlib + try: import importlib.resources as pkg_resources except ImportError: import importlib_resources as pkg_resources # Try backported to PY<37 `importlib_resources`. +import yaml -def load(name): - """Loads a callable from a string with format `module_path:callable_name`. - """ - mod_name, attr_name = name.split(":") +def load(name): + '''Loads a callable from a string with format `module_path:callable_name`.''' + mod_name, attr_name = name.split(':') mod = importlib.import_module(mod_name) fn = getattr(mod, attr_name) return fn class Spec(): - """ A specification for a particular instance of the environment. + '''A specification for a particular instance of the environment.''' - """ - - def __init__(self, id, entry_point=None, config_entry_point=None): - """Used in function "register". + def __init__(self, idx, entry_point=None, config_entry_point=None): + '''Used in function 'register'. Args: - id (str): The official environment ID + idx (str): The official environment ID entry_point (cls, str): The Python entrypoint of the environment class. - config_entry_point (str): The config to pass to the environment class. - - """ - self.id = id + config_entry_point (str): The config to pass to the environment class.''' + self.idx = idx self.entry_point = entry_point self.config_entry_point = config_entry_point def __repr__(self): - """Defines a class instance's string representation. - - """ - return "Spec({})".format(self.id) + '''Defines a class instance's string representation.''' + return f'Spec({self.idx})' def get_config(self): - """Fetches config (as dict) for the registered class. - - """ + '''Fetches config (as dict) for the registered class.''' if isinstance(self.config_entry_point, str): - if self.config_entry_point.endswith(".yaml"): + if self.config_entry_point.endswith('.yaml'): # Specified as file path. - mod_name, config_name = self.config_entry_point.split(":") + mod_name, config_name = self.config_entry_point.split(':') with pkg_resources.open_text(mod_name, config_name) as f: config = yaml.load(f, Loader=yaml.FullLoader) else: - # Specified as "module_path:config_dict_name". + # Specified as 'module_path:config_dict_name'. config = load(self.config_entry_point) elif self.config_entry_point is None: # No default config. config = {} else: - raise Exception("Config type {} is not supported.".format(self.config_entry_point)) + raise Exception(f'Config type {self.config_entry_point} is not supported.') return config def make(self, *args, **kwargs): - """Instantiates an instance of the registered class with appropriate kwargs. - - """ + '''Instantiates an instance of the registered class with appropriate kwargs.''' if self.entry_point is None: - raise Exception('Attempting to make deprecated env {}.'.format(self.id)) + raise Exception(f'Attempting to make deprecated env {self.idx}.') if callable(self.entry_point): obj = self.entry_point(*args, **kwargs) else: @@ -79,7 +69,7 @@ def make(self, *args, **kwargs): obj = cls(*args, **kwargs) # Make the instance aware of which spec it came from. spec = copy.deepcopy(self) - if hasattr(obj, "spec"): + if hasattr(obj, 'spec'): obj.instance_spec = spec else: obj.spec = spec @@ -87,80 +77,62 @@ def make(self, *args, **kwargs): class Registry(): - """Register a callable by ID. - - """ + '''Register a callable by ID.''' def __init__(self): self.specs = {} def make(self, path, *args, **kwargs): - """Create an instance of the registered callable by id `path`. - - """ + '''Create an instance of the registered callable by id `path`.''' spec = self.specs[path] obj = spec.make(*args, **kwargs) return obj def all(self): - """Returns all registered callables. - - """ + '''Returns all registered callables.''' return self.specs.values() def spec(self, path): - """Returns spec of the registered callable by id. - - """ + '''Returns spec of the registered callable by id.''' if ':' in path: - mod_name, id = path.split(':') + mod_name, _ = path.split(':') try: importlib.import_module(mod_name) - except: - raise Exception('''A module ({}) was specified for the environment but was not found,make sure the - package is installed with `pip install` before calling `gym.make()`'''.format(mod_name)) + except BaseException: + raise Exception(f'''A module ({mod_name}) was specified for the environment but was not found,make sure the + package is installed with `pip install` before calling `gym.make()`''') else: - id = path + idx = path try: - return self.specs[id] - except: - raise Exception("Key not found in registry.") - - def register(self, id, **kwargs): - """Saves a reference to the callable with a id. - - """ - if id in self.specs: - raise Exception('Cannot re-register id: {}'.format(id)) - self.specs[id] = Spec(id, **kwargs) - - -def register(id, **kwargs): - """Saves the callable to the global registry. - - """ - return registry.register(id, **kwargs) + return self.specs[idx] + except BaseException: + raise Exception('Key not found in registry.') + def register(self, idx, **kwargs): + '''Saves a reference to the callable with a id.''' + if idx in self.specs: + raise Exception(f'Cannot re-register id: {idx}') + self.specs[idx] = Spec(idx, **kwargs) -def make(id, *args, **kwargs): - """Creates an instance of the callable from global registry. - """ - return registry.make(id, *args, **kwargs) +def register(idx, **kwargs): + '''Saves the callable to the global registry.''' + return registry.register(idx, **kwargs) -def spec(id): - """Gets the spec of the callable from global registry. +def make(idx, *args, **kwargs): + '''Creates an instance of the callable from global registry.''' + return registry.make(idx, *args, **kwargs) - """ - return registry.spec(id) +def spec(idx): + '''Gets the spec of the callable from global registry.''' + return registry.spec(idx) -def get_config(id): - """Gets the config of the callable from global registry. - """ - return registry.spec(id).get_config() +def get_config(idx): + '''Gets the config of the callable from global registry.''' + return registry.spec(idx).get_config() # Registry: global registry (singleton) used by functions `register`, `make`, `spec` and `get_config`. diff --git a/safe_control_gym/utils/utils.py b/safe_control_gym/utils/utils.py index 2dad183cb..434146e0a 100644 --- a/safe_control_gym/utils/utils.py +++ b/safe_control_gym/utils/utils.py @@ -1,6 +1,5 @@ -"""Miscellaneous utility functions. +'''Miscellaneous utility functions.''' -""" import argparse import datetime import time @@ -10,26 +9,25 @@ import random import subprocess import sys -import munch -import yaml + +import gymnasium as gym import imageio +import munch import numpy as np import torch +import yaml def mkdirs(*paths): - """Makes a list of directories. + '''Makes a list of directories.''' - """ for path in paths: if not os.path.exists(path): os.makedirs(path) def eval_token(token): - """Converts string token to int, float or str. - - """ + '''Converts string token to int, float or str.''' if token.isnumeric(): return int(token) try: @@ -38,23 +36,22 @@ def eval_token(token): return token -def read_file(file_path, sep=","): - """Loads content from a file (json, yaml, csv, txt). - +def read_file(file_path, sep=','): + '''Loads content from a file (json, yaml, csv, txt). + For json & yaml files returns a dict. Ror csv & txt returns list of lines. - - """ + ''' if len(file_path) < 1 or not os.path.exists(file_path): return None # load file - f = open(file_path, "r") - if "json" in file_path: + f = open(file_path, 'r') + if 'json' in file_path: data = json.load(f) - elif "yaml" in file_path: + elif 'yaml' in file_path: data = yaml.load(f, Loader=yaml.FullLoader) else: - sep = sep if "csv" in file_path else " " + sep = sep if 'csv' in file_path else ' ' data = [] for line in f.readlines(): line_post = [eval_token(t) for t in line.strip().split(sep)] @@ -68,9 +65,7 @@ def read_file(file_path, sep=","): def merge_dict(source_dict, update_dict): - """Merges updates into source recursively. - - """ + '''Merges updates into source recursively.''' for k, v in update_dict.items(): if k in source_dict and isinstance(source_dict[k], dict) and isinstance( v, dict): @@ -80,39 +75,31 @@ def merge_dict(source_dict, update_dict): def get_time(): - """Gets current timestamp (as string). - - """ + '''Gets current timestamp (as string).''' start_time = datetime.datetime.now() - time = str(start_time.strftime("%Y_%m_%d-%X")) + time = str(start_time.strftime('%Y_%m_%d-%X')) return time def get_random_state(): - """Snapshots the random state at any moment. - - """ + '''Snapshots the random state at any moment.''' return { - "random": random.getstate(), - "numpy": np.random.get_state(), - "torch": torch.get_rng_state() + 'random': random.getstate(), + 'numpy': np.random.get_state(), + 'torch': torch.get_rng_state() } def set_random_state(state_dict): - """Resets the random state for experiment restore. - - """ - random.setstate(state_dict["random"]) - np.random.set_state(state_dict["numpy"]) - torch.torch.set_rng_state(state_dict["torch"]) + '''Resets the random state for experiment restore.''' + random.setstate(state_dict['random']) + np.random.set_state(state_dict['numpy']) + torch.torch.set_rng_state(state_dict['torch']) def set_seed(seed, cuda=False): - """General seeding function for reproducibility. - - """ - assert seed is not None, "Error in set_seed(...), provided seed not valid" + '''General seeding function for reproducibility.''' + assert seed is not None, 'Error in set_seed(...), provided seed not valid' random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) @@ -122,60 +109,54 @@ def set_seed(seed, cuda=False): def set_dir_from_config(config): - """Creates a output folder for experiment (and save config files). - - Naming format: {root (e.g. results)}/{tag (exp id)}/{seed}_{timestamp}_{git commit id} + '''Creates a output folder for experiment (and save config files). - """ + Naming format: {root (e.g. results)}/{tag (exp id)}/{seed}_{timestamp}_{git commit id} + ''' # Make run folder (of a seed run for an experiment) - seed = str(config.seed) if config.seed is not None else "-" - timestamp = str(datetime.datetime.now().strftime("%b-%d-%H-%M-%S")) + seed = str(config.seed) if config.seed is not None else '-' + timestamp = str(datetime.datetime.now().strftime('%b-%d-%H-%M-%S')) try: commit_id = subprocess.check_output( - ["git", "describe", "--tags", "--always"] - ).decode("utf-8").strip() + ['git', 'describe', '--tags', '--always'] + ).decode('utf-8').strip() commit_id = str(commit_id) - except: - commit_id = "-" - run_dir = "seed{}_{}_{}".format(seed, timestamp, commit_id) + except BaseException: + commit_id = '-' + run_dir = f'seed{seed}_{timestamp}_{commit_id}' # Make output folder. config.output_dir = os.path.join(config.output_dir, config.tag, run_dir) mkdirs(config.output_dir) # Save config. - with open(os.path.join(config.output_dir, 'config.yaml'), "w") as file: + with open(os.path.join(config.output_dir, 'config.yaml'), 'w') as file: yaml.dump(munch.unmunchify(config), file, default_flow_style=False) # Save command. with open(os.path.join(config.output_dir, 'cmd.txt'), 'a') as file: - file.write(" ".join(sys.argv) + "\n") + file.write(' '.join(sys.argv) + '\n') def set_seed_from_config(config): - """Sets seed, only set if seed is provided. - - """ + '''Sets seed, only set if seed is provided.''' seed = config.seed if seed is not None: set_seed(seed, cuda=config.use_gpu) def set_device_from_config(config): - """Sets device, using GPU is set to `cuda` for now, no specific GPU yet. - - """ + '''Sets device, using GPU is set to `cuda` for now, no specific GPU yet.''' use_cuda = config.use_gpu and torch.cuda.is_available() - config.device = "cuda" if use_cuda else "cpu" + config.device = 'cuda' if use_cuda else 'cpu' def save_video(name, frames, fps=20): - """Convert list of frames (H,W,C) to a video. + '''Convert list of frames (H,W,C) to a video. Args: name (str): path name to save the video. frames (list): frames of the video as list of np.arrays. fps (int, optional): frames per second. - - """ - assert ".gif" in name or ".mp4" in name, "invalid video name" + ''' + assert '.gif' in name or '.mp4' in name, 'invalid video name' vid_kwargs = {'fps': fps} h, w, c = frames[0].shape video = np.stack(frames, 0).astype(np.uint8).reshape(-1, h, w, c) @@ -183,15 +164,14 @@ def save_video(name, frames, fps=20): def str2bool(val): - """Converts a string into a boolean. + '''Converts a string into a boolean. Args: val (str|bool): Input value (possibly string) to interpret as boolean. Returns: bool: Interpretation of `val` as True or False. - - """ + ''' if isinstance(val, bool): return val elif val.lower() in ('yes', 'true', 't', 'y', '1'): @@ -199,13 +179,11 @@ def str2bool(val): elif val.lower() in ('no', 'false', 'f', 'n', '0'): return False else: - raise argparse.ArgumentTypeError("[ERROR] in str2bool(), a Boolean value is expected") + raise argparse.ArgumentTypeError('[ERROR] in str2bool(), a Boolean value is expected') def unwrap_wrapper(env, wrapper_class): - """Retrieve a ``VecEnvWrapper`` object by recursively searching. - - """ + '''Retrieve a ``VecEnvWrapper`` object by recursively searching.''' env_tmp = env while isinstance(env_tmp, gym.Wrapper): if isinstance(env_tmp, wrapper_class): @@ -215,9 +193,7 @@ def unwrap_wrapper(env, wrapper_class): def is_wrapped(env, wrapper_class): - """Check if a given environment has been wrapped with a given wrapper. - - """ + '''Check if a given environment has been wrapped with a given wrapper.''' return unwrap_wrapper(env, wrapper_class) is not None def sync(i, start_time, timestep): diff --git a/setup.py b/setup.py new file mode 100644 index 000000000..8ca23c269 --- /dev/null +++ b/setup.py @@ -0,0 +1,27 @@ +from setuptools import setup + +setup(name='safe_control_gym', + version='2.0.0', + install_requires=[ + 'matplotlib', + 'munch', + 'pyyaml', + 'imageio', + 'dict-deep', + 'scikit-optimize', + 'scikit-learn', + 'gymnasium', + 'torch', + 'gpytorch', + 'tensorboard', + 'casadi', + 'pybullet', + 'numpy', + 'cvxpy', + 'pytope', + 'Mosek', + 'termcolor', + 'pytest', + 'pre-commit', + ], + ) diff --git a/tests/test_build.py b/tests/test_build.py new file mode 100644 index 000000000..c9ea04831 --- /dev/null +++ b/tests/test_build.py @@ -0,0 +1,8 @@ +def test_imports(): + import safe_control_gym + import safe_control_gym.controllers + import safe_control_gym.envs + import safe_control_gym.envs.env_wrappers + import safe_control_gym.experiments + import safe_control_gym.safety_filters + import safe_control_gym.utils diff --git a/tests/test_examples/test_cbf.py b/tests/test_examples/test_cbf.py new file mode 100644 index 000000000..cf2f6f487 --- /dev/null +++ b/tests/test_examples/test_cbf.py @@ -0,0 +1,24 @@ +import sys + +import pytest + +from examples.cbf.cbf_experiment import run + + +@pytest.mark.parametrize('SYS', ['cartpole']) +@pytest.mark.parametrize('ALGO', ['ppo', 'sac']) +@pytest.mark.parametrize('SAFETY_FILTER', ['cbf', 'cbf_nn']) +def test_cbf(SYS, ALGO, SAFETY_FILTER): + sys.argv[1:] = ['--algo', ALGO, + '--task', SYS, + '--safety filter', SAFETY_FILTER, + '--overrides', + f'./examples/cbf/config_overrides/{SYS}_config.yaml', + f'./examples/cbf/config_overrides/{ALGO}_config.yaml', + f'./examples/cbf/config_overrides/{SAFETY_FILTER}_config.yaml', + '--kv_overrides', + 'sf_config.max_num_steps=10', + 'sf_config.num_episodes=2', + 'sf_config.train_iterations=10'] + + run(plot=False, training=True, n_episodes=None, n_steps=10, curr_path='./examples/cbf', save_data=False) diff --git a/tests/test_examples/test_lqr.py b/tests/test_examples/test_lqr.py new file mode 100644 index 000000000..c666ada51 --- /dev/null +++ b/tests/test_examples/test_lqr.py @@ -0,0 +1,21 @@ +import sys + +import pytest + +from examples.lqr.lqr_experiment import run + + +@pytest.mark.parametrize('SYS', ['cartpole', 'quadrotor_2D', 'quadrotor_3D']) +@pytest.mark.parametrize('TASK', ['stabilization', 'tracking']) +@pytest.mark.parametrize('ALGO', ['lqr', 'ilqr']) +def test_lqr(SYS, TASK, ALGO): + SYS_NAME = 'quadrotor' if 'quadrotor' in SYS else SYS + sys.argv[1:] = ['--algo', ALGO, + '--task', SYS_NAME, + '--overrides', + f'./examples/lqr/config_overrides/{SYS}/{SYS}_{TASK}.yaml', + f'./examples/lqr/config_overrides/{SYS}/{ALGO}_{SYS}_{TASK}.yaml', + '--kv_overrides', + 'algo_config.max_iterations=2' + ] + run(gui=False, n_episodes=None, n_steps=10, save_data=False) diff --git a/tests/test_examples/test_mpsc.py b/tests/test_examples/test_mpsc.py new file mode 100644 index 000000000..a7bb778f4 --- /dev/null +++ b/tests/test_examples/test_mpsc.py @@ -0,0 +1,26 @@ +import sys + +import pytest + +from examples.mpsc.mpsc_experiment import run + + +@pytest.mark.parametrize('SYS', ['cartpole', 'quadrotor_2D']) +@pytest.mark.parametrize('TASK', ['stab', 'track']) +@pytest.mark.parametrize('ALGO', ['lqr', 'pid', 'ppo', 'sac']) +@pytest.mark.parametrize('SAFETY_FILTER', ['linear_mpsc']) +@pytest.mark.parametrize('MPSC_COST', ['one_step_cost']) +def test_mpsc(SYS, TASK, ALGO, SAFETY_FILTER, MPSC_COST): + if SYS == 'cartpole' and ALGO == 'pid': + pytest.skip('PID is designed for quadrotors and does not function for the carpole system.') + SYS_NAME = SYS if SYS == 'cartpole' else 'quadrotor' + sys.argv[1:] = ['--task', SYS_NAME, + '--algo', ALGO, + '--safety_filter', SAFETY_FILTER, + '--overrides', + f'./examples/mpsc/config_overrides/{SYS}/{SYS}_{TASK}.yaml', + f'./examples/mpsc/config_overrides/{SYS}/{ALGO}_{SYS}.yaml', + f'./examples/mpsc/config_overrides/{SYS}/{SAFETY_FILTER}_{SYS}.yaml', + '--kv_overrides', f'sf_config.cost_function={MPSC_COST}' + ] + run(plot=False, training=False, n_episodes=None, n_steps=5, curr_path='./examples/mpsc') diff --git a/tests/test_examples/test_no_controller.py b/tests/test_examples/test_no_controller.py new file mode 100644 index 000000000..4a174acc9 --- /dev/null +++ b/tests/test_examples/test_no_controller.py @@ -0,0 +1,13 @@ +import sys + +from examples.no_controller.verbose_api import run as verbose_run + + +def test_verbose_api_cartpole(): + sys.argv[1:] = ['--task', 'cartpole', '--overrides', './examples/no_controller/verbose_api.yaml'] + verbose_run() + + +def test_verbose_api_quadrotor(): + sys.argv[1:] = ['--task', 'quadrotor', '--overrides', './examples/no_controller/verbose_api.yaml'] + verbose_run() diff --git a/tests/test_examples/test_pid.py b/tests/test_examples/test_pid.py new file mode 100644 index 000000000..42d73bfbf --- /dev/null +++ b/tests/test_examples/test_pid.py @@ -0,0 +1,27 @@ +import sys + +import pytest + +from examples.pid.pid_experiment import run + + +@pytest.mark.parametrize('SYS', ['quadrotor_2D', 'quadrotor_3D']) +@pytest.mark.parametrize('TASK', ['stabilization', 'tracking']) +def test_pid(SYS, TASK): + sys.argv[1:] = ['--algo', 'pid', + '--task', 'quadrotor', + '--overrides', + f'./examples/pid/config_overrides/{SYS}/{SYS}_{TASK}.yaml', + ] + run(gui=False, n_episodes=None, n_steps=10, save_data=False) + + +def test_3D_pid_custom_trajectory_tracking(): + sys.argv[1:] = ['--algo', 'pid', + '--task', 'quadrotor', + '--overrides', + './examples/pid/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml', + '--kv_overrides', + 'task_config.task_info.trajectory_type=custom' + ] + run(gui=False, n_episodes=None, n_steps=10, save_data=False) diff --git a/tests/test_examples/test_rl.py b/tests/test_examples/test_rl.py new file mode 100644 index 000000000..81ec07198 --- /dev/null +++ b/tests/test_examples/test_rl.py @@ -0,0 +1,21 @@ +import sys + +import pytest + +from examples.rl.rl_experiment import run + + +@pytest.mark.parametrize('SYS', ['cartpole', 'quadrotor_2D', 'quadrotor_3D']) +@pytest.mark.parametrize('TASK', ['stab', 'track']) +@pytest.mark.parametrize('ALGO', ['ppo', 'sac', 'safe_explorer_ppo']) +def test_rl(SYS, TASK, ALGO): + SYS_NAME = 'quadrotor' if 'quadrotor' in SYS else SYS + sys.argv[1:] = ['--algo', ALGO, + '--task', SYS_NAME, + '--overrides', + f'./examples/rl/config_overrides/{SYS}/{SYS}_{TASK}.yaml', + f'./examples/rl/config_overrides/{SYS}/{ALGO}_{SYS}.yaml', + '--kv_overrides', + 'algo_config.training=False', + ] + run(gui=False, plot=False, n_episodes=None, n_steps=10, curr_path='./examples/rl')