AKF-SR: Adaptive Kalman filtering-based successor representation

Implementation of the AKF-SR algorithm presented in the paper AKF-SR: Adaptive Kalman filtering-based successor representation to play tag in OpenAI's multi-agent particle environment.

Code Structure

AKF-SR Codebase
│   AKFSR.py - AKF-SR model
│   AKFSR_tag.py - Run the AKFSR model
|   make_env.py - Create the environment by importing a multiagent environment as an OpenAI Gym-like object
│   AKFSR_initial.py - Initialize AKF-SR's parameters
│   general_utilities.py - Utilities for  directory creation, JSON data dumping, and managing time-series data in AKFSR_tag.py 
│   simple_tag_utilities.py- Utilities for the environment

Paper Citation:

@article{malekzadeh2022akf,
  title={AKF-SR: Adaptive Kalman filtering-based successor representation},
  author={Malekzadeh, Parvin and Salimibeni, Mohammad and Hou, Ming and Mohammadi, Arash and Plataniotis, Konstantinos N},
  journal={Neurocomputing},
  volume={467},
  pages={476--490},
  year={2022},
  publisher={Elsevier}
}

Credits

• The implementation of the multiagent environment is from OpenAI.