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.

AlphaGo Zero implementation using Flux.jl

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

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

Deep Reinforcement Learning for Chess

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.

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

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

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

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

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

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

Java based alpha zero reinforcement learning. The generic base module allows implementation of any adversary board game. Example implementation for Tic Tac Toe.

Alpha Zero Implemented through Libtorch/pytorch