Deep RL for Chess

Structure

models/ Contains policy and evaluation models. Policy and eval models both come in a fully connected version with embeddings and a convolutional version. The CNN model is better for "obvious" positions while the fully connected version seems to have better accuracy.

datasets/ Contains datasets. You can add your own! Place a pgn formatted filed in the datasets/pgn directory and train using python train --dataset NAME for file NAME.pgn. The datasets/epds directory contain Extended Position Description files for each of these datasets. And the datasets/csvs directory contains a csv version.

logs/ Each folder is named by date and time it was created. In each folder are subfolders checkpoints/ and summaries/ which contain model checkpoints and logging summaries respectively. Use the command tensorboard --logdir=FOLDER where FOLDER is your date and time to view these logs and checkpoints in a nice format.

train_eval.py Trains the evaluation network. Use python train_eval.py --help to get a list of flags.

train_policy.py Trains the policy network. Use python train_policy.py --help to get a list of flags.

Will add a script to play against a trained model soonish.

Requirements

tensorflow (clearly)
tflearn
python-chess
numpy
pandas
tqdm

vkramanuj/beta-chess

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