A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
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Updated
Jan 1, 2025 - Jupyter Notebook
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
javascript gobang AI,JS五子棋AI,源码+教程,基于Alpha-Beta剪枝算法(不是神经网络)
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
One of the strongest Gomoku/Renju playing engine
An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku
A PyTorch implementation of DeepMind's AlphaZero agent to play Go and Gomoku board games
A pytorch based Gomoku game model. Alpha Zero algorithm based reinforcement Learning and Monte Carlo Tree Search model.
[IEEE ToG] MiniZero: An AlphaZero and MuZero Training Framework
Reinforcing Your Learning of Reinforcement Learning
A relatively strong gomoku AI (2018 ver). New engine: https://github.com/dhbloo/rapfi
A serious Gomoku board game AI written in C++
一个由 Minimax 算法和 Alpha-Beta 剪枝驱动的五子棋 AI,最高支持 14 层深度搜索,包含段位系统和双人对战模式。
alphaGo版本的五子棋(gobang, gomoku)
SlowRenju is a Gomoku/Renju engine, which supports the new protocol of Gomocup.
♟♟♟♟♟ A Gomoku game AI based on Monte Carlo Tree Search, can be trained on policy-value network now. 一个蒙特卡洛树搜索算法实现的五子棋 AI,现可用神经网络训练模型。
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