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
Implements self-play reinforcement learning via Monte Carlo Tree Search (MCTS) combined with neural network training in a modular architecture where games and frameworks are pluggable through subclassing `Game.py` and `NeuralNet.py`. The core training loop (`Coach.py`) alternates between self-play episodes guided by MCTS and neural network optimization, supporting PyTorch and Keras backends with configurable hyperparameters for simulation depth, batch size, and learning rates. Includes pretrained models and enables direct evaluation against baseline opponents through the pit interface.
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
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