alpha-zero-general and AlphaGo-Zero-Gobang
These two tools are competitors, as both offer implementations of AlphaZero, with tool B specifically focusing on Gobang, which is also supported by the more general tool A.
About alpha-zero-general
suragnair/alpha-zero-general
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
This project offers a flexible implementation of the AlphaZero algorithm, enabling you to train an AI to play any two-player turn-based board game. It takes the rules of a game and a neural network model as input, and outputs a highly skilled AI player that learns through self-play. This is ideal for AI researchers, game designers, or hobbyists looking to create strong AI opponents for custom or existing board games.
About AlphaGo-Zero-Gobang
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work