mcts
There are 383 repositories under mcts topic.
hijkzzz/Awesome-LLM-Strawberry
A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 🍓 and reasoning techniques.
suragnair/alpha-zero-general
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
junxiaosong/AlphaZero_Gomoku
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
opendilab/LightZero
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
chauvinSimon/My_Bibliography_for_Research_on_Autonomous_Driving
Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"
s-casci/tinyzero
Easily train AlphaZero-like agents on any environment you want!
hrpan/tetris_mcts
MCTS project for Tetris
dylandjian/SuperGo
A student implementation of Alpha Go Zero
DataCanvasIO/Hypernets
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
QueensGambit/CrazyAra
A Deep Learning UCI-Chess Variant Engine written in C++ & Python :parrot:
vgarciasc/mcts-viz
Visualization of MCTS algorithm applied to Tic-tac-toe.
sungyubkim/Deep_RL_with_pytorch
A pytorch tutorial for DRL(Deep Reinforcement Learning)
initial-h/AlphaZero_Gomoku_MPI
An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku
thuxugang/doudizhu
AI斗地主
kaesve/muzero
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.
akolishchak/doom-net-pytorch
Reinforcement learning models in ViZDoom environment
manyoso/allie
Allie: A UCI compliant chess engine
zjeffer/chess-deep-rl
Research project: create a chess engine using Deep Reinforcement Learning
CGLemon/Sayuri
AlphaZero based engine for the game of Go (圍棋/围棋).
PuYuuu/vehicle-interaction-decision-making
The decision-making of multiple vehicles at intersection bases on level-k game and MCTS
blanyal/alpha-zero
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.
lowrollr/turbozero
fast + parallel AlphaZero in JAX
Urinx/ReinforcementLearning
Reinforcing Your Learning of Reinforcement Learning
YoujiaZhang/AlphaGo-Zero-Gobang
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
rlglab/minizero
MiniZero: An AlphaZero and MuZero Training Framework
kobanium/Ray
Computer go engine using Monte-Carlo Tree Search (MCTS)
gorisanson/quoridor-ai
Quoridor AI based on Monte Carlo tree search
masouduut94/MCTS-agent-python
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.
kobanium/TamaGo
Computer go engine using Monte-Carlo Tree Search written in Python3.
CGLemon/pyDLGO
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。
yangboz/godpaper
:monkey_face: An AI chess-board-game framework(by many programming languages) implementations.
TianHongZXY/CoRe
[ACL 2023] Solving Math Word Problems via Cooperative Reasoning induced Language Models (LLMs + MCTS + Self-Improvement)
coreylowman/synthesis
A rust implementation of AlphaZero algorithm
hr0nix/omega
A number of agents (PPO, MuZero) with a Perceiver-based NN architecture that can be trained to achieve goals in nethack/minihack environments.
michaelbzms/MonteCarloTreeSearch
A fast C++ impementation of Monte Carlo Tree Search with abstract classes that a user of this library can extend in order to use it. To demonstrate it I apply it to the game of Quoridor.