alpha-zero-general | A clean implementation based on AlphaZero for any game | Machine Learning library

alpha-zero-general is a Jupyter Notebook library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. alpha-zero-general has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

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, TensorFlow and Chainer. 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,chainer}/NNet.py. Coach.py contains the core training loop and MCTS.py performs the Monte Carlo Tree Search. The parameters for the self-play can be specified in main.py. Additional neural network parameters are in othello/{pytorch,keras,tensorflow,chainer}/NNet.py (cuda flag, batch size, epochs, learning rate etc.).