A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

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

Optimizing inference proxy for LLMs

MuZero

Monte Carlo tree search in JAX

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

A tool for retrosynthetic planning

A curated list of Monte Carlo tree search papers with implementations.

A Hearthstone AI based on Monte Carlo tree search and neural nets written in modern C++.

LLM verified with Monte Carlo Tree Search

Plug-and-play tree search for agents

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

Board game AI implementations using Monte Carlo Tree Search

A pytorch based Gomoku game model. Alpha Zero algorithm based reinforcement Learning and Monte Carlo Tree Search model.

[IEEE ToG] MiniZero: An AlphaZero and MuZero Training Framework

A novel parallel UCT algorithm with linear speedup and negligible performance loss.

fast + parallel AlphaZero in JAX

Deep active inference agents using Monte-Carlo methods

Project of Siggraph Asia 2020 paper: Scene Mover: Automatic Move Planning for Scene Arrangement by Deep Reinforcement Learning

Animal Fight Chess Game（斗兽棋） written in rust.

Various C# implementations of game AI

Quoridor AI based on Monte Carlo tree search

Generate correct code from unit-tests

Computer go engine using Monte-Carlo Tree Search (MCTS)

Pytorch Implementation of Stochastic MuZero for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.

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

Omaha Poker functionality+some features for PokerRL Reinforcement Learning card framwork

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 and planning problems. In this project I used a board game called "HEX" as a platform to test different simulation strategies in MCTS field.

🌳 Python implementation of single-player Monte-Carlo Tree Search.

♟♟♟♟♟ A Gomoku game AI based on Monte Carlo Tree Search, can be trained on policy-value network now. 一个蒙特卡洛树搜索算法实现的五子棋 AI，现可用神经网络训练模型。