monte-carlo-tree-search

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

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++.

A tool for retrosynthetic planning

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.

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

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

Various C# implementations of game AI

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

Deep active inference agents using Monte-Carlo methods

Monte Carlo Tree Search Based AI Connect 4 Bot

A game framework based on AlphaZero/TensorFlow.js runs in browser to demonstrate tic-tac-toe AI game. Use a pre-trained model or train from scratch. Ported from suragnair/alpha-zero-general (Python)

Algorithms for building strong immutable AIs for round-based games.

Solving board games like Connect4 using Deep Reinforcement Learning

The recommendation engine for Python software stacks and Dependency Monkey in project Thoth.

A Monte Carlo Tree Search AI for the game 2048

UCThello - a board game demonstrator (Othello variant) with computer AI using Monte Carlo Tree Search (MCTS) with UCB (Upper Confidence Bounds) applied to trees (UCT in short)

Quoridor AI based on Monte Carlo tree search

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

An AI for Reversi.

Using self-play, MCTS, and a deep neural network to create a hearthstone ai player

This is a pip package implementing Reinforcement Learning algorithms in non-stationary environments supported by the OpenAI Gym toolkit.

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.

Repo with code for Ludii's built-in AIs.

♟♟♟♟♟ 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。