Honors College Thesis

 

Monte Carlo Counterfactual Regret Minimization applied to Clue Public Deposited

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

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  • 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
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