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Comparing Randomization Strategies for Search-Control Parameters in Monte-Carlo Tree Search
- Source :
- 2019 IEEE Conference on Games (CoG), 1-8, STARTPAGE=1;ENDPAGE=8;TITLE=2019 IEEE Conference on Games (CoG), CoG
- Publication Year :
- 2019
-
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.
- Subjects :
- Randomization
Theoretical computer science
Monte-Carlo tree search
Computer science
Monte Carlo tree search
A domain
search-control parameter
Diversification (marketing strategy)
randomization
computer.software_genre
General game playing
Tree (data structure)
General Game Playing
Control parameters
computer
Subjects
Details
- Language :
- English
- ISSN :
- 23254270
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE Conference on Games (CoG)
- Accession number :
- edsair.doi.dedup.....001b6ba77c3144d63e65efa8b23a80ac