chess ai
1.0.0
I've been playing chess for a very long time, and ever since I started CS, I've always wanted to create a chess bot. I've finally done it ?.
Here's a video where I (white) get crushed by my bot (black) .
Here's its chess.com profile: https://www.chess.com/member/chessables_with_chat_gpt.
git clone https://github.com/samliu21/chess-ai. Navigate into the directory with cd chess-ai.python -m venv . and activate it with source bin/activate.python -m pip install -r requirements.txt.cd gui and call python main.py to play!I used the official Lichess database, which contained games in a standard PGN format. Here is the data cleaning process:
pgn-extract to add FENs after each moveFor more information, look at the data_cleaning folder.
Initially, I tried to create a board evaluation neural network to pair with a minimax algorithm. There were two issues with this approach:
The evaluation network didn't perform to my expectations. It could detect material imbalances but couldn't detect simple checkmates.
Due to the large action space in chess, the minimax algorithm is very slow, even when optimzied with alpha-beta pruning.
Together, these factors prompted me to scrap this initial idea and try another.
The GUI was hand-made using the pygame and python-chess modules.
This architecture was largely inspired by this Standford paper.
The AI uses two models. They both receive a board position as input and output an 8x8 matrix of softmax probabilities. The "from model" predicts the square to be moved out of and the "to model" predicts the square to be moved into.
This approach is best illustrated with an example. Consider the starting board position and the move: Nf3. The evaluation of this move is the product of the value at the g1 square of the from model and the value at the f3 square of the to model.
Among all legal moves, the largest product is the selected move.
The neural networks consist of six convolutional layers, followed by two affine layers and an output layer. A more detailed sketch of the architecture can be found below:
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 8, 8, 12)] 0 []
conv2d (Conv2D) (None, 8, 8, 32) 3488 ['input_1[0][0]']
batch_normalization (BatchNorm (None, 8, 8, 32) 128 ['conv2d[0][0]']
alization)
activation (Activation) (None, 8, 8, 32) 0 ['batch_normalization[0][0]']
conv2d_1 (Conv2D) (None, 8, 8, 64) 18496 ['activation[0][0]']
batch_normalization_1 (BatchNo (None, 8, 8, 64) 256 ['conv2d_1[0][0]']
rmalization)
activation_1 (Activation) (None, 8, 8, 64) 0 ['batch_normalization_1[0][0]']
conv2d_2 (Conv2D) (None, 8, 8, 256) 147712 ['activation_1[0][0]']
batch_normalization_2 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_2[0][0]']
rmalization)
activation_2 (Activation) (None, 8, 8, 256) 0 ['batch_normalization_2[0][0]']
concatenate (Concatenate) (None, 8, 8, 512) 0 ['activation_2[0][0]',
'activation_2[0][0]']
conv2d_3 (Conv2D) (None, 8, 8, 256) 1179904 ['concatenate[0][0]']
batch_normalization_3 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_3[0][0]']
rmalization)
activation_3 (Activation) (None, 8, 8, 256) 0 ['batch_normalization_3[0][0]']
concatenate_1 (Concatenate) (None, 8, 8, 320) 0 ['activation_3[0][0]',
'activation_1[0][0]']
conv2d_4 (Conv2D) (None, 8, 8, 256) 737536 ['concatenate_1[0][0]']
batch_normalization_4 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_4[0][0]']
rmalization)
activation_4 (Activation) (None, 8, 8, 256) 0 ['batch_normalization_4[0][0]']
concatenate_2 (Concatenate) (None, 8, 8, 288) 0 ['activation_4[0][0]',
'activation[0][0]']
conv2d_5 (Conv2D) (None, 8, 8, 256) 663808 ['concatenate_2[0][0]']
batch_normalization_5 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_5[0][0]']
rmalization)
activation_5 (Activation) (None, 8, 8, 256) 0 ['batch_normalization_5[0][0]']
dense (Dense) (None, 8, 8, 256) 65792 ['activation_5[0][0]']
batch_normalization_6 (BatchNo (None, 8, 8, 256) 1024 ['dense[0][0]']
rmalization)
dense_1 (Dense) (None, 8, 8, 64) 16448 ['batch_normalization_6[0][0]']
batch_normalization_7 (BatchNo (None, 8, 8, 64) 256 ['dense_1[0][0]']
rmalization)
dense_2 (Dense) (None, 8, 8, 1) 65 ['batch_normalization_7[0][0]']
batch_normalization_8 (BatchNo (None, 8, 8, 1) 4 ['dense_2[0][0]']
rmalization)
softmax (Softmax) (None, 8, 8, 1) 0 ['batch_normalization_8[0][0]']
==================================================================================================
Total params: 2,839,013
Trainable params: 2,836,131
Non-trainable params: 2,882
__________________________________________________________________________________________________
