Dynamic Maze Solver (COMP6247)

This repository contains all the code used to solve the dynamic maze challenge for COMP6247 Reinforcement & Online Learning.

Usage

The configuration file (config.py) contains self-explanatory variables for configuring the program.

Run the following (from this directory) to execute the pretrained agent on a maze.

python -m src.eval  # run pretrained version

To train the agent, run the following (from this directory). This will complete one epoch of training.

python -m src.train  # run pretrained version

Alternatively, you can also run

make eval

or

make train

to execute the same functions.

Training make take a few minutes.

A copy of the q-table obtained for the optimal path has been included in the data/ directory.

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Repository Structure

The repository is structured with all the source code in the src/ directory, which is the further subdivided according to functionality. The agent is implemented in the agent/ directory. Visualization functions can be found in display/. Other additional functionality is in lib/.

Log File

An agent can be logged to a file by calling the log_agent() function in the lib package.

The agent observation at every time-step is encoded in a text representation for easy logging.

X marks the position of the agent. W denotes a wall.

A digit (0-2) denotes the life-time of a fire.

If the block is empty, it is an empty path.

Some examples are shown below.

agent surrounded by walls:

|W|W|W|

|W|X|W|

|W|W|W|


agent surrounded by empty paths with walls in the diagonals:

|W| |W|

| |X| |

|W| |W|


agent with one fire:

|W|W|W|

| |X|2|

|W|W|W|


agent with two fires:

|W|1|W|

|0|X|W|

|W| |W|