An environment of the board game Go using OpenAI's Gym API
-
Updated
May 3, 2022 - Python
An environment of the board game Go using OpenAI's Gym API
AlphaZero implementation for Othello, Connect-Four and Tic-Tac-Toe based on "Mastering the game of Go without human knowledge" and "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by DeepMind.
Learning from zero (mostly based off of AlphaZero) in General Game Playing.
Using self-play, MCTS, and a deep neural network to create a hearthstone ai player
Deep Reinforcement Learning for Chess
A clean and easy implementation of MuZero, AlphaZero and Self-Play reinforcement learning algorithms for any game.
An implementation of the AlphaZero algorithm for adversarial games to be used with the machine learning framework of your choice
A gomoku AI based on Alpha Zero paper.
HybridAlpha - a mix between AlphaGo Zero and AlphaZero for multiple games
A simplified version of Shogi with the AI is trained by alpha-zero-type training method
hyper optimized alpha zero implementation to play gomoku (distributed training with ray, mcts with cython)
Omok using MCTS (UCT, PUCT)
Pokémon battle simulator that uses the technique proposed in AlphaZero to beat the opponent.
Reinforcement Learning applied to 3-Player Chinese Checkers
This is a repository about Reinforcement Learning in which two agents, a Deep-Q-Network agent and an Alpha-Zero agent, learn to play Bullet Chess.
reinforcement learning applied to simple board games
The board game Go implemented in JAX for fast game processing and machine learning training.
AlphaZero for ultimate tic-tac-toe.
Add a description, image, and links to the alpha-zero topic page so that developers can more easily learn about it.
To associate your repository with the alpha-zero topic, visit your repo's landing page and select "manage topics."