An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

MuZero

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

AI斗地主

A student implementation of Alpha Go Zero

An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

MCTS project for Tetris

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.

Reinforcing Your Learning of Reinforcement Learning

Reinforcement learning models in ViZDoom environment

Deep Learning big homework of UCAS

Easily train AlphaZero-like agents on any environment you want!

Monte Carlo Tree Search for tic tac toe

Here are some Python implementations of Gomoku AIs, including MCTS, Minimax and Genetic Alg.

Computer go engine using Monte-Carlo Tree Search written in Python3.

基於深度學習的 GTP 圍棋（围棋）引擎，KGS 指引文件以及演算法教學。

Monte Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a search tree accordingly. It has already had a profound impact on Artificial Intelligence (AI) approaches for domains that can be represented as trees of sequential decisions, particularly games …

Program for playing chess in the console against AI or human opponents

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