ChessAI

An implementation of a neural network chess opponent.

Usage

 pip3 install python-chess torch torchvision numpy flask
 # then...
 ./play.py   # runs webserver on localhost:5000

TODOs

1. Look into minimax and rating.
2. Decide on a good number of epochs.

Advanced:

3. Add RL self play learning support.
4. Roll out search beyond 1-ply.

Implementation

The trained net is in nets/value.pth. It takes in a serialized board and outputs a range from -1 to 1. -1 means black is win, 1 means white is win.

Serialization

We serialize the board into a 8x8x5 bitvector. See state.py for how.

Training Set

The value function was trained on 100K board positions for now. More trained functions expected in the near future. Data is from http://www.kingbase-chess.net/