Displaying 1 to 3 from 3 results

alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4

  •    Python

A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play based reinforcement learning based on the AlphaGo Zero paper (Silver et al). It is designed to be easy to adopt for any two-player turn-based adversarial game and any deep learning framework of your choice. A sample implementation has been provided for the game of Othello in PyTorch, Keras and TensorFlow. An accompanying tutorial can be found here. We also have implementations for GoBang and TicTacToe. To use a game of your choice, subclass the classes in Game.py and NeuralNet.py and implement their functions. Example implementations for Othello can be found in othello/OthelloGame.py and othello/{pytorch,keras,tensorflow}/NNet.py.

Zerofish - An implementation of the AlphaZero algorithm for chess

  •    Python

Currently under construction. Currently uses a completely different model than the one from the paper! This model has a very different layout than the one from the paper. Significantly reduced number of parameters in value and policy output heads. Completely different action space. Does not deal with under-promotions. Action space is absolute compared to the relative to moving piece action spaced used in the paper.

AnimalChess - Animal Fight Chess Game(斗兽棋) written in rust.

  •    Rust

Animal Fight Chess Game(斗兽棋) written in rust, using Alpha-Beta-Pruning algorithm, and implement AlphaZero algorithm for training. It need rust nightly version.






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