Using Reinforcement Learning to solve mazes of arbitrary sizes. Use the variables mx and my
in the Python code to configure the mazes' size in blocks and use img_dx and img_dy to
specify the amount of pixels of the output image. Here is an example maze featuring
40x40 blocks in 500x500 pixels:
maze_runner.py: Generates a random maze and then uses a Uniform Random Walk Policy and the Iterative Policy Evaluation algorithm to solve the maze. The empty maze and the solution is stored as a result a series of PNG images.maze_runner_q_mp.py: Generates a random maze and then uses Q-Learning to solve the maze. This version of the solution also utilizes Python's multi-processing capabilities to speed-up image saving. The results are a numbered series of PNG files. Assumptions to make it more "realistic" than the "godmode" dynamic programming version that you find in the other sourcemaze_runner.py
grid_world.py: Helper class taken from the Udemy RL course: It represents a Grid World consisting basically of two dictionaries: one containing all possible actions at each position of the grid and one containing the rewards used for the RL algorithm.maze_generator.py: A set of helper functions to generate random mazes in the form of two dimensional lists and to save them as PNG files.
The animated mazes shown at http://www.sy2002.de/maze have been
generated using maze_runner.py. The output are many PNG files named mrunner_2_*.png.
If you have ImageMagick installed, you can use the
following commands to create an Animated GIF:
python maze_runner.py
convert -loop 0 -delay 10 mrunner_2_*.png animated.gif
rm *.png
The more advanced maze_runner_q_mp.py could also be used to generate animated mazes.
But by default, it is generating "heatmaps" of the Q-Learning's "learning walk" instead.
If you want maze_runner_q_mp.py to generate animated mazes instead, then
this
line of code has to be changed: Move it inside the for-loop and use i instead of
episode as format variable.