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Permanent link (DOI): https://doi.org/10.7939/R39P2WF2V

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Regret Minimization in Games and the Development of Champion Multiplayer Computer Poker-Playing Agents Open Access

Descriptions

Other title
Subject/Keyword
regret minimization
poker
multiagent systems
artificial intelligence
game theory
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Gibson, Richard G
Supervisor and department
Szafron, Duane (Computing Science)
Examining committee member and department
Larson, Kate (Cheriton School of Computer Science, University of Waterloo)
Holte, Robert (Computing Science)
Mueller, Martin (Computing Science)
Bowling, Michael (Computing Science)
Szafron, Duane (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2013-12-20T14:37:53Z
Graduation date
2014-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Recently, poker has emerged as a popular domain for investigating decision problems under conditions of uncertainty. Unlike traditional games such as checkers and chess, poker exhibits imperfect information, varying utilities, and stochastic events. Because of these complications, decisions at the poker table are more analogous to the decisions faced by humans in everyday life. In this dissertation, we investigate regret minimization in extensive-form games and apply our work in developing champion computer poker agents. Counterfactual Regret Minimization (CFR) is the current state-of-the-art approach to computing capable strategy profiles for large extensive-form games. Our primary focus is to advance our understanding and application of CFR in domains with more than two players. We present four major contributions. First, we provide the first set of theoretical guarantees for CFR when applied to games that are not two-player zero-sum. We prove that in such domains, CFR eliminates strictly dominated plays. In addition, we provide a modification of CFR that is both more efficient and can lead to stronger strategies than were previously possible. Second, we provide new regret bounds for CFR, present three new CFR sampling variants, and demonstrate their efficiency in several different domains. Third, we prove the first set of sufficient conditions that guarantee CFR will minimize regret in games with imperfect recall. Fourth, we generalize three previous game tree decomposition methods, present a new decomposition method, and demonstrate their improvement empirically over standard techniques. Finally, we apply the work in this thesis to construct three-player Texas hold'em agents and enter them into the Annual Computer Poker Competition. Our agents won six out of the seven three-player events that we entered from the 2010, 2011, 2012, and 2013 computer poker competitions.
Language
English
DOI
doi:10.7939/R39P2WF2V
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Richard Gibson and Duane Szafron. Regret minimization in multiplayer extensive games. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI), pages 2802–2803, 2011.Richard G. Gibson and Duane Szafron. On strategy stitching in large extensive form multiplayer games. In Advances in Neural Information Processing Systems (NIPS) 24, pages 100–108, 2011.Richard Gibson, Marc Lanctot, Neil Burch, Duane Szafron, and Michael Bowling. Generalized sampling and variance in counterfactual regret minimization. In Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (AAAI), pages 1355–1361, 2012.Marc Lanctot, Richard Gibson, Neil Burch, and Michael Bowling. No-regret learning in extensive-form games with imperfect recall. In Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML), pages 65–72, 2012.Richard Gibson, Neil Burch, Marc Lanctot, and Duane Szafron. Efficient Monte Carlo counterfactual regret minimization in games with many player actions. In Advances in Neural Information Processing Systems 25 (NIPS), pages 1889–1897, 2012.

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