PDF] MONTE CARLO TREE SEARCH: A TUTORIAL

This tutorial provides an introduction to MCTS, including a review of its history and relationship to a more general simulation-based algorithm for Markov decision processes (MDPs), a demonstration of the basic mechanics of the algorithms via decision trees and the game of tic-tac-toe; and its use in AlphaGo and AlphaZero. Monte Carlo tree search (MCTS) is a general approach to solving game problems, playing a central role in Google DeepMind’s AlphaZero and its predecessor AlphaGo, which famously defeated the (human) world Go champion Lee Sedol in 2016 and world #1 Go player Ke Jie in 2017. Starting from scratch without using any domain-specific knowledge (other than the game rules), AlphaZero defeated not only its ancestors in Go but also the best computer programs in chess (Stockfish) and shogi (Elmo). In this tutorial, we provide an introduction to MCTS, including a review of its history and relationship to a more general simulation-based algorithm for Markov decision processes (MDPs) published in a 2005 Operations Research article; a demonstration of the basic mechanics of the algorithms via decision trees and the game of tic-tac-toe; and its use in AlphaGo and AlphaZero.

Applied Sciences, Free Full-Text

Monte Carlo Tree Search

PDF] MONTE CARLO TREE SEARCH: A TUTORIAL

Monte Carlo Tree Search – beginners guide

A comparison of Monte Carlo tree search and rolling horizon optimization for large-scale dynamic resource allocation problems - ScienceDirect

Buildings, Free Full-Text

Entropy, Free Full-Text

16.MCTS Tutorial, PDF, Applied Mathematics

Monte Carlo Tree Search (MCTS) Tutorial

Monte Carlo Tree Search with Last-Good-Reply Policy for Cognitive Optimization of Cloud-Ready Optical Networks

Monte Carlo tree search for materials design and discovery, MRS Communications

Monte Carlo tree search for materials design and discovery, MRS Communications

PDF] Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks