Comparing Randomization Strategies for Search-Control Parameters in Monte-Carlo Tree Search

Chiara F. Sironi*, Mark H. M. Winands

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

Monte-Carlo Tree Search (MCTS) has been applied successfully in many domains. Previous research has shown that adding randomization to certain components of MCTS might increase the diversification of the search and improve the performance. In a domain that tackles many games with different characteristics, like General Game Playing (GGP), trying to diversify the search might be a good strategy. This paper investigates the effect of randomizing search-control parameters for MCTS in GGP. Four different randomization strategies are compared and results show that randomizing parameter values before each simulation has a positive effect on the search in some of the tested games. Moreover, parameter randomization is compared with on-line parameter tuning.
Original languageEnglish
Title of host publication2019 IEEE Conference on Games (CoG)
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Print)9781728118840
DOIs
Publication statusPublished - Aug 2019
EventIEEE Conference on Games (CoG) 2019 - People's Palace, Queen Mary University of London, London, United Kingdom
Duration: 20 Aug 201923 Aug 2019

Publication series

SeriesIEEE Conference on Computational Intelligence and Games
ISSN2325-4270

Conference

ConferenceIEEE Conference on Games (CoG) 2019
Abbreviated titleCOG
Country/TerritoryUnited Kingdom
CityLondon
Period20/08/1923/08/19

Keywords

  • Monte-Carlo tree search
  • search-control parameter
  • randomization
  • General Game Playing

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