An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

MuZero

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

Implement AlphaZero/AlphaGo Zero methods on Chinese chess.

Easily train AlphaZero-like agents on any environment you want!

♟️ Vectorized RL game environments in JAX (NeurIPS23)

A Multi-threaded Implementation of AlphaZero (C++)

Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku

A pytorch based Gomoku game model. Alpha Zero algorithm based reinforcement Learning and Monte Carlo Tree Search model.

A gobang robot based on reinforcement learning.

A functional Alpha Zero that plays Othello using Keras

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.

A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)

fast + parallel AlphaZero in JAX

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

PyTorch implementation of AlphaZero Connect from scratch (with results)

A PyTorch implementation of DeepMind's AlphaZero agent to play Go and Gomoku board games

Meta-Zeta是一个基于强化学习的五子棋(Gobang)模型，主要用以了解AlphaGo Zero的运行原理的Demo，即神经网络是如何指导MCTS做出决策的，以及如何自我对弈学习。源码+教程

alphaGo版本的五子棋(gobang, gomoku)