Student Work
Monte-Carlo Search in Games
PublicDownloadable Content
open in viewerThis paper implements and analyzes four algorithms for improving computer play of the board game Go. These algorithms use machine pattern learning to find better Monte-Carlo simulation policies for use with Monte-Carlo Tree Search. Two of these algorithms maximize individual move strength, and two minimize overall simulation error. These algorithms are tested using UCT on 9x9 Go with 3x3 patterns.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
- Creator
- Publisher
- Identifier
- E-project-042909-035846
- Advisor
- Year
- 2009
- Center
- Sponsor
- Date created
- 2009-04-29
- Location
- Budapest
- Resource type
- Major
- Rights statement
Relations
- In Collection:
Items
Items
Thumbnail | Title | Visibility | Embargo Release Date | Actions |
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dvander_mqp_d09_paper.pdf | Public | Download | ||
dvander_mqp_d09_source.tar.gz | Public | Download | ||
dvander_mqp_d09_presentation.pdf | Public | Download |
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