Open-Zero

Table of Content

Introduction

Features

Installation

Quickstart

Contributors

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Introduction

Open-Zero is a research project that aims to make an open source implementation of AlphaZero and MuZero's methods from DeepMind on the game of chess.

We use Deep Reinforcement Learning methods such as Asynchronous Advantage Actor-Crique or A3C.

A3C methods, unlike synchronous methods, use multithreading to get a larger amount of training data, making the process of having promising result with the AI faster.

The AI instanciates as much workers as possible, with each of theses workers working on a copy of the global network. Once a worker has finished an episode of training, it updates the global network and starts a new episode with a copy of the latest global network. This method allows for a faster training, but the wider variety of training data gives it a higher quality of training and a better result.

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Features

Training

The AI trains by playing against itself using A3C methods.

Testing

We can test the AI multiple ways:

• Watch the AI play against itself
• Make an evaluation of a game using Stockfish

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Installation

Clone Repository

git clone https://github.com/PoCInnovation/Open-Zero.git
cd Open-Zero

Build the docker image

docker build . -t openzero

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Quickstart

The launch-project.sh script is the tool you use to do almost everything in this project. Get the usage help by doing:

docker run openzero

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Contributors

Gino Ambigaipalan → Github

Jean-Baptiste Debize → Github

Nell Fauveau → Github

Bogdan Guillemoles → Github

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