Neural chess
Reinforcement Learning based chess engine.

Personal project to build a chess engine based using reinforcement learning.

The idea is to some sort replicate the system built by DeepMind with AlphaZero. I'm aware that the computational resources to achieve their results is huge, but my aim it's simply to reach an amateur chess level performance (about 1200-1400 Elo), not state of the art.

At this moment, the approach I'm using is based on pre-training a model using self-play data of a Stockfish algorithm. Later, the idea is to put two models to play agaisnt each other and make selection/merges of weights (RL part).

If you want to reuse this code on your project, and have any doubt here you will find some explanation about the most important classes. Also, feel free to open an issue on this repo to ask.

Work in progress

Requirements

The necessary python packages (ATM) are listed in the requirements file. You can install them with

pip3 install -r requirements.txt

Tensorflow is also needed, but you must install either tensorflow or tensorflow-gpu (for the development I used >= TF 2.0).

Also, you need to download the specific stockfish binary for your platform, for automating this made a script to automatically download it.

cd res
chmod +x get_stockfish.sh
./get_stockfish.sh linux   #or "mac", depending of your platform. 

If you want to download it manually, you have to put the stockfish executable under res/stockfish-10-64 path, in order to the training script to detect it.

Training

DISCLAIMER: This is under development and can still contains bugs or inefficiencies and modifications are being made.

DISCLAIMER 2: As soon as I get acceptable results, I will also share weights/datasets with this code.

For training an agent in a supervised way you will need a saved dataset of games. The script gen_data_stockfish.py is made for generating a JSON with this. This script will play (and record) several games using two Stockfish instances. Execute it first to create this dataset (take a look at it's possible arguments).

The main purpose of this part is to pre-train the model to make the policy head of the network to reliably predict the game outcome. This will be useful during the self-play phase as the MCTS will make better move policies (reducing the training time).

cd src/chessrl
python gen_data_stockfish.py ../../data/dataset_stockfish.json --games 100

Once we have a training dataset (generated or your own adapted), start the supervised training with:

cd src/chessrl
python supervised.py ../../data/models/model1 ../../data/dataset_stockfish.json --epochs 2 --bs 4

Once we have a pretrained model, we can move to the self-play phase. The incharged of this process is the selfplay.py script, which will fire up a instance of the model which play agaisnt itself and after each one, makes a training round (saving the model and the results). Please, take a look at the possible arguments. However, here you have an example. (Keep in mind that this is an expensive process which takes a considerable amount of time per move).

cd src/chessrl
python selfplay.py ../../data/models/model1-superv --games 100

How do I view the progress?

The neural network trainning evolution can be monitored with Tensorboard, simply:

tensorboard --logdir data/models/model1/train

(And set the "Horizontal axis" to "WALL" for viewing all the diferent runs.)

Also, in the same model directory you will find a gameplays.json file which contains the recorded training games of the model. With this, we can study its behaviour over time.

Can I play against the agent?

Yes. Under src/webplayer you will find a Flask app which deploys a web interface to play against the trained agent. There is another README with more information.

Literature

1. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, Silver, D. et al. https://arxiv.org/pdf/1712.01815.pdf
2. Mastering the game of Go without human knowledge. Silver, D. et al. https://www.nature.com/articles/nature24270.epdf?author_access_token=VJXbVjaSHxFoctQQ4p2k4tRgN0jAjWel9jnR3ZoTv0PVW4gB86EEpGqTRDtpIz-2rmo8-KG06gqVobU5NSCFeHILHcVFUeMsbvwS-lxjqQGg98faovwjxeTUgZAUMnRQ