Leela Chess Zero

Leela Chess Zero Leela chess zero is an algorithm made to train agents on the Leela Chess Engine. The Leela Chess Zero’s neural network is largely based on the DeepMind’s AlphaGo Zero and AlphaZero architecture. There are however some changes. It should be trained in a competition with multiple versions of its past self.

The policy/model assumes that the environment is a MultiAgent Chess environment, that has a discrete action space and returns an observation as a dictionary with two keys:

• obs that contains an observation under either the form of a state vector or an image
• action_mask that contains a mask over the legal actions

It should also implement a get_stateand a set_state function, used in the MCTS implementation.

The model used in AlphaZero trainer should extend TorchModelV2 and implement the method compute_priors_and_value.

References

• AlphaZero: https://arxiv.org/abs/1712.01815
• LeelaChessZero: https://github.com/LeelaChessZero/lc0

Installation

conda create -n rllib-leela-chess python=3.10
conda activate rllib-leela-chess
pip install -r requirements.txt
pip install -e '.[development]'

Usage

[Leela Chess Zero Example]()