Monte Carlo Counterfactual Regret Minimization applied to Clue

Honors College Thesis

Público Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/honors_college_theses/xk81js52b

Descriptions

Attribute Name	Values
Creator	Martin, Benjamin
Abstract	This document analyzes the application of Monte Carlo Counterfactual Regret Minimization (MCCFR) in the game of Hasboro’s Clue. As a partially observable stochastic multiplayer game, Clue is well-suited for MCCFR methods. MCCFR has previously been shown to be effective in beating top human players around the world in No-Limit Texas Hold’em. We have found that an MCCFR agent proves to be superior in win rate over a heuristic model counting alternative and two baseline random agents using choice sampling and regret matching. Keywords: clue, cluedo, MCCFR, monte carlo, partially observable games
Resource Type	Honors College Thesis
Fecha de Emisión	2020-06-12
Degree Level	Bachelor's
Degree Name	Honors Bachelor of Science (H.B.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2021
Advisor	Tadepalli, Prasad
Committee Member	Fern, Alan Walkingshaw, Eric
Non-Academic Affiliation	Oregon State University. Honors College
Declaración de derechos	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]

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	MartinBenjaminP2020.pdf	2020-12-03	Público	Descargar