Maze Solver using Naive Reinforcement Learning with Q-Table construction
Full detailed article at - https://towardsdatascience.com/maze-rl-d035f9ccdc63
Sample video at - https://www.youtube.com/watch?v=kOIVUGqz7gI
This is an implementation of the Q-Learning algorithm in Reinforcement Learning from scratch using python, numpy and opencv for visualization. Everything including the game-world , visualization and AI is in one python file dqn_grid_world.py with the following dependencies-
- numpy
- opencv
- moviepy
The AI is implemented in numpy, while the rest is used for game world and visualization.
Simply run python dqn_grid_world.py. It will start a progress bar training the agent. The training process video is written out in the same directory.
Feel free to use the code if you find it useful! :)