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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.

betago - BetaGo: AlphaGo for the masses, live on github.

  •    Python

BetaGo lets you run your own Go engine. It downloads Go games for you, preprocesses them, trains a model on data, for instance a neural network using keras, and serves the trained model to an HTML front end, which you can use to play against your own Go bot. Test BetaGo by running the following commands. It should start a playable demo in your browser! This bot plays reasonable moves, but is still rather weak.

deep_learning_and_the_game_of_go - Code and other material for the book "Deep Learning and the Game of Go"

  •    Python

This repository is first and foremost a comprehensive machine learning framework for the game of Go, focussing on deep learning techniques. What you'll find here is a library that builds up from the game-play basics to very advanced techniques. In particular, you find code for early approaches in game AI, intermediate techniques using deep learning, to implementations of AlphaGo and AlphaGo Zero - all presented in one common framework. You can install this library with pip and follow the examples in the code folder. On the other hand, this repository at the same time contains Code, and sample chapters for the book "Deep Learning and the Game of Go" (Manning), available for early access here, which ties into the library and teaches its components bit by biy. If you're following the code samples from the book, check out the branches for individual chapters.

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