alpha-zero-general and reversi-alpha-zero
These are competitors, as both repositories provide implementations of AlphaZero methods for game playing, with "alpha-zero-general" offering a generalized framework for various games and "reversi-alpha-zero" focusing specifically on Reversi.
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 reversi-alpha-zero
mokemokechicken/reversi-alpha-zero
Reversi reinforcement learning by AlphaGo Zero methods.
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