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

Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey

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

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

MCTS project for Tetris

A student implementation of Alpha Go Zero

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

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 pytorch tutorial for DRL(Deep Reinforcement Learning)

An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku

AI斗地主

Research project: create a chess engine using Deep Reinforcement Learning

A clean implementation of MuZero and AlphaZero following the AlphaZero General framework. Train and Pit both algorithms against each other, and investigate reliability of learned MuZero MDP models.

The decision-making of multiple vehicles at intersection bases on level-k game and MCTS

Reinforcement learning models in ViZDoom environment

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

AlphaZero based engine for the game of Go (圍棋/围棋).

Allie: A UCI compliant chess engine

AlphaGo-Zero-Gobang 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

fast + parallel AlphaZero in JAX

Reinforcing Your Learning of Reinforcement Learning

This is a repo for showcasing using MCTS with LLMs to solve gsm8k problems

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.