A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation

An environment of the board game Go using OpenAI's Gym API

AlphaZero implementation for Othello, Connect-Four and Tic-Tac-Toe based on "Mastering the game of Go without human knowledge" and "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by DeepMind.

Learning from zero (mostly based off of AlphaZero) in General Game Playing.

AlphaZero-like AI solution for playing Ultimate Tic-Tac-Toe in the browser

AlphaGo Zero implementation using Flux.jl

Using self-play, MCTS, and a deep neural network to create a hearthstone ai player

Deep Reinforcement Learning for Chess

A clean and easy implementation of MuZero, AlphaZero and Self-Play reinforcement learning algorithms for any game.

An implementation of the AlphaZero algorithm for adversarial games to be used with the machine learning framework of your choice

A gomoku AI based on Alpha Zero paper.

Simple C++ project that includes header only implementations of Monte Carlo Tree Search(MCTS), Temporal Difference Learning, Minimax, and other agents to evaluate their performance in various two player games. Also includes an implementation of AlphaZero for Connect4.

HybridAlpha - a mix between AlphaGo Zero and AlphaZero for multiple games

A simplified version of Shogi with the AI is trained by alpha-zero-type training method

hyper optimized alpha zero implementation to play gomoku (distributed training with ray, mcts with cython)

presentation on AlphaZero for AI seminar (http://ktiml.mff.cuni.cz/~bartak/ui_seminar/)

Java based alpha zero reinforcement learning. The core module suppports implementing any adversary board game. Example implementation for Tic Tac Toe.

Visual representation of RTS game, supported by deep reinforcement learning algorithm Alpha Zero written in python.