YoujiaZhang/AlphaGo-Zero-Gobang
AlphaGo-Zero-Gobang 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
This project helps demonstrate how an AI can learn to play the game of Gobang (five-in-a-row) through self-play, inspired by AlphaGo Zero. It takes the rules of Gobang as input and produces an AI player capable of learning optimal moves and playing against a human. This is for AI enthusiasts, students, or researchers interested in understanding the practical application of reinforcement learning and Monte Carlo Tree Search (MCTS) in game AI.
110 stars. No commits in the last 6 months.
Use this if you want to see a concrete, runnable example of how AlphaGo Zero's principles, like neural networks guiding MCTS and self-play, are applied to teach an AI to master a board game.
Not ideal if you're looking for a general-purpose AI development framework or a competitive, production-ready Gobang AI.
Stars
110
Forks
10
Language
Python
License
MIT
Category
Last pushed
May 29, 2025
Commits (30d)
0
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