Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 85-86 |
| Number of pages | 2 |
| Journal | IEEE Computational Intelligence Magazine |
| Volume | 19 |
| Issue number | 1 |
| Early online date | 8 Jan 2024 |
| DOIs | |
| Publication status | Published - Feb 2024 |
| Externally published | Yes |
Bibliographical note
Yunlong Zhao and Chengpeng Hu contributed equally to this work.Funding
This work was supported in part by the National Key R&D Program 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.