Playing With Monte-Carlo Tree Search [AI-eXplained]

Yunlong Zhao; Chengpeng Hu; Jialin Liu

doi:10.1109/MCI.2023.3328150

Playing With Monte-Carlo Tree Search [AI-eXplained]

Yunlong Zhao
, Chengpeng Hu
, Jialin Liu (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

6 Citaten (Scopus)

Samenvatting

This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore.

Originele taal-2	Engels
Artikelnummer	10384538
Pagina's (van-tot)	85-86
Aantal pagina's	2
Tijdschrift	IEEE Computational Intelligence Magazine
Volume	19
Nummer van het tijdschrift	1
DOI's	https://doi.org/10.1109/MCI.2023.3328150
Status	Gepubliceerd - feb. 2024
Extern gepubliceerd	Ja

Financiering

This work was supported in part by the National KeyR&DProgram of China under Grant 2023YFE0106300, in part by the National Natural Science Foundation of China under Grant 62250710682, in part by Guangdong Provincial Key Laboratory under Grant 2020B121201001, in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X386, in part by SUSTech Undergraduate Teaching Quality and Reform Project under Grant SJZLGC20210, and in part by the Research Institute of Trustworthy Autonomous Systems. Yunlong Zhao and Chengpeng Hu contributed equally to thiswork

Financiers	Financiernummer
National Natural Science Foundation of China	62250710682

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Playing With Monte-Carlo Tree Search [AI-eXplained]

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