An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
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Updated
Apr 24, 2024 - Python
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess.
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
Easily train AlphaZero-like agents on any environment you want!
♟️ Vectorized RL game environments in JAX
A Multi-threaded Implementation of AlphaZero
Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)
An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku
A pytorch based Gomoku game model. Alpha Zero algorithm based reinforcement Learning and Monte Carlo Tree Search model.
A functional Alpha Zero that plays Othello using Keras
A gobang robot based on reinforcement learning.
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.
PyTorch implementation of AlphaZero Connect from scratch (with results)
Learning from zero (mostly based off of AlphaZero) in General Game Playing.
fast + parallel AlphaZero in JAX
Meta-Zeta是一个基于强化学习的五子棋(Gobang)模型,主要用以了解AlphaGo Zero的运行原理的Demo,即神经网络是如何指导MCTS做出决策的,以及如何自我对弈学习。源码+教程
alphaGo版本的五子棋(gobang, gomoku)
A fast, generalized, and modified implementation of Deepmind's distinguished AlphaZero in PyTorch.
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