Alpha Zero General (any game, any framework!)

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.

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.

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}/NNet.py (cuda flag, batch size, epochs, learning rate etc.).

To start training a model for Othello:

python main.py

Choose your framework and game in main.py.

Implemented Games

• Othello
• Tic Tac Toe
• Gobang

Experiments

We trained a PyTorch model for 6x6 Othello (~80 iterations, 100 episodes per iteration and 25 MCTS simulations per turn). This took about 3 days on an NVIDIA Tesla K80. The pretrained model (PyTorch) can be found in pretrained_models/pytorch/. You can play a game against it using pit.py. Below is the performance of the model against a random and a greedy baseline with the number of iterations.

A concise description of our algorithm can be found here.

Contributing

While the current code is fairly functional, we could benefit from the following contributions:

• Game logic files for more games that follow the specifications in Game.py, along with their neural networks
• Neural networks in other frameworks
• Pre-trained models for different game configurations
• An asynchronous version of the code- parallel processes for self-play, neural net training and model comparison.
• Asynchronous MCTS as described in the paper

Contributors and Credits

• Shantanu Thakoor and Megha Jhunjhunwala helped with core design and implementation.
• Shantanu Kumar contributed TensorFlow and Keras models for Othello.
• Evgeny Tyurin contributed rules and a trained model for TicTacToe.
• MBoss contributed rules and a model for GoBang.

Thanks to pytorch-classification and progress.

AlphaGo / AlphaZero Events

• February 8, 2018 - Solving Alpha Go Zero + TensorFlow, Kubernetes-based Serverless AI Models on GPU (Youtube / Slides)