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

Lightweight AlphaZero implementation for Gomoku with MCTS + residual policy/value network, including ready-to-run 9×9 and 15×15 training presets.

AlphaZero self-play + C++ minimax engine for Gomoku · PyTorch, MCTS, pybind11, GCP

Game cờ caro 15x15,19x19

A GPU-accelerated AlphaZero implementation for Renju (Gomoku/Five-in-a-Row) using JAX/Flax with a real-time web-based game interface.

This is a CLI Gomoku game program written in python. It supports playing with either AI or human, while AI is driven by Minimax algorithm coupled with alpha-beta pruning.

Gomoku AI player powered by Monte Carlo, UCB, and a priority factor for gameplay decisions.

Gomoku AI from Scratch

A game-playing agent capable of playing Five-in-a-Row (Gomoku) (AI)

Gomoku Game with AI and GUI.

Gomoku web game

An Gomoku GUI to support Katagomo.And it's expected to be modified to support other variants.

Implemented an AI-powered Gomoku game featuring multiple strategies including a reflex agent, alpha-beta pruning, Q-learning, and Monte Carlo Tree Search (MCTS).

🎮 基于MCP协议的五子棋AI对战平台 | AI vs AI实时对战系统 | Claude AI智能博弈 | Web实时观战 | 27步史诗级对战记录

A gymnasium environment for gomoku

Pygame で開発された高機能な五目並べ（連珠ルール対応）ゲーム。AI 対戦、脅威可視化、定石表示などの機能を搭載。 A feature-rich Gomoku (Five-in-a-Row) game implementing Renju rules, built with Pygame. Features AI opponents, threat visualization, and joseki display.

trained gomoku models under gui

Adversarial search algorithms to develop an agent that plays the Gomoku game.

This is a pure JAX implementation of (Gumbel) AlphaZero, designed for efficiency and simplicity.