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](http://web.stanford.edu/~surag/posts/alphazero.html). 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.).