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

MuZero

Monte Carlo tree search in JAX

Optimizing inference proxy for LLMs

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

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

A tool for retrosynthetic planning

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

A library of reasoning algorithms for AI agents

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.

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

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

fast + parallel AlphaZero in JAX

Deep active inference agents using Monte-Carlo methods

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 …

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

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

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

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.