Universal MCT wrapper script for all Windows 10/11 versions from 1507 to 21H2!

A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 🍓 and reasoning techniques.

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)

MuZero

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

Latest Advances on System-2 Reasoning

[NIPS'25 Spotlight] Mulberry, an o1-like Reasoning and Reflection MLLM Implemented via Collective MCTS

Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey

ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search (NeurIPS 2024)

The release codes of LA-MCTS with its application to Neural Architecture Search.

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"

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

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.

A Go A.I. based on MCTS WITHOUT Neural Networks

MCTS project for Tetris

This is the repository that contains the source code for the Self-Evaluation Guided MCTS for online DPO.

Python Implementations of Monte Carlo Tree Search

[NeurIPS 2023] We use large language models as commonsense world model and heuristic policy within Monte-Carlo Tree Search, enabling better-reasoned decision-making for daily task planning problems.

A Deep Learning UCI-Chess Variant Engine written in C++ & Python :parrot:

A student implementation of Alpha Go Zero

Minimalistic Go MCTS Engine

Visualization of MCTS algorithm applied to Tic-tac-toe.

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

SE-Agent is a self-evolution framework for LLM Code agents. It enables trajectory-level evolution to exchange information across reasoning paths via Revision, Recombination, and Refinement, expanding the search space and escaping local optima. On SWE-bench Verified, it achieves SOTA performance

A simple package to allow users to run Monte Carlo Tree Search on any perfect information domain

No description available

A pytorch tutorial for DRL(Deep Reinforcement Learning)

An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku

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