AlphaGo for Chess

AlphaGo for Chess is a chess AI agent that implements a deep neural network with the combination of the MCTS algorithm.

This work follows the main ideas in the AlphaGo Zero article.

The work resulted in an agent with ~2000 elo rating by evaluating only 1200 board positions per move and was tested against stockfish (strongest available open source AI) manually.

<h3> Check out my blog explaining the work in detail
here

</h3></b>


Expert chess games taken from: http://caissabase.co.uk/

Representation implemented according to alphazero article.

resnet architecture implemented as explained here(alphazero):

“A general reinforcement learning algorithm that masters chess, shogi and Go through self-play”

https://discovery.ucl.ac.uk/id/eprint/10069050/ https://discovery.ucl.ac.uk/id/eprint/10069050/1/alphazero_preprint.pdf

run requirements: install requirements.txt file.

Playing

Download the nn weights:
https://drive.google.com/drive/folders/1iMF6H7JiasNJ-Db5j5LJPgaV3tOIJ-n-?usp=sharing

The elo ranking is currently ~2000 - tested against stockfish at https://chessui.com/.

Set config.json “torch_nn_path” to the backbone .pth weights path (largest file). Make sure the heads’ weights (_policy_network.pth, _value_network.pth) are also in the same folder.

run play.py, standard run parameters to play against nn with mcts as white:
–whuman
standard run parameters to play against nn with mcts as black:
–bhuman

When playing your games are automatically saved to the my_chess/games folder, both an image of the board and its fen are saved each turn.

You can resume a game from the saved fens by adding these parameters:
-board “<your-fen>“
e.g: -board “8/4Q3/8/7k/5K2/8/8/8 w - - 0 1”

You can undo your move by clicking the right mouse button.
Note that when playing against a player this will undo a the last move only but when playing against the AI it will undo both the player’s last move and the AI’s.