A chess artificial intelligence using the MTD-f algorithm for move selection and that can learn from old games. It uses the python-chess library for board representation, move generation, zobrist hashing and reading .pgn files.

Temporal difference learning, a type of reinforcement learning, was used to train the AI. The inspiration for this work was this paper.

This is a match played by the AI against itself. White has a search depth of 2, and black has a search depth of 3. Black wins.

1. Clone the repo.
2. Install python and virtualenv.
3. Download pypy binary.
4. Run virtualenv -p <pypy binary location here> venv to set up your virtual environment.
5. Run source venv/bin/activate to activate your virtual environment.
6. Run pip install -r requirements.txt to install dependencies.
7. Run pypy main.py to play against the AI and pypy self_play.py to watch the AI play against itself.
8. Run deactivate to deactivate the virtual environment.

Note: main.py and self_play.py both have MAX_DEPTH constants that can be adjusted to change the AI's strength. Increase it to increase the strength and decrease it to decrease the strength.

1. Find and download a database of chess games in PGN format, or use this.
2. Place the downloaded .pgn file in the project root.
3. In config.py, set GAMES_FILE_NAME to the name of the .pgn file. Feel free to tweak with other parameters too.
4. Run pypy learn.py to learn.