Michael Bowling

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Manuela M. VelosoHerbert A. Simon University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Neil BurchResearch scientist, DeepMindVerified email at ualberta.ca
Michael Bradley JohansonDeepMindVerified email at google.com
Joel VenessGoogle DeepMindVerified email at google.com
Marc G. BellemareGoogle BrainVerified email at google.com
Nolan BardUniversity of AlbertaVerified email at ualberta.ca
Marc LanctotResearch Scientist, Google DeepMindVerified email at google.com
Kevin WaughCarnegie Mellon UniversityVerified email at cs.cmu.edu
Martin SchmidCharles University in PragueVerified email at kam.mff.cuni.cz
Viliam LisýCzech Technical University in PragueVerified email at fel.cvut.cz
Dustin MorrillComputing Science Graduate Student, University of AlbertaVerified email at ualberta.ca
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaVerified email at cs.ualberta.ca
Brett BrowningNREC, RI, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Marlos C. MachadoDeepMindVerified email at google.com
Daniel LizotteAssistant Professor of Computer Science, University of Western OntarioVerified email at uwo.ca
Erin J. TalvitieAssociate Professor of Computer Science, Harvey Mudd CollegeVerified email at cs.hmc.edu
Trevor DavisPhD student, University of AlbertaVerified email at ualberta.ca
Duane SzafronProfessor of Computing Science, University of AlbertaVerified email at ualberta.ca
James R BruceGoogle ResearchVerified email at google.com
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu

Michael Bowling

University of Alberta / DeepMind

Verified email at ualberta.ca - Homepage

Artificial Intelligence Machine Learning Game Theory Reinforcement Learning Computer Games


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The arcade learning environment: An evaluation platform for general agents MG Bellemare, Y Naddaf, J Veness, M Bowling Journal of Artificial Intelligence Research 47, 253-279, 2013	1836	2013
Multiagent learning using a variable learning rate M Bowling, M Veloso Artificial Intelligence 136 (2), 215-250, 2002	878	2002
Deepstack: Expert-level artificial intelligence in heads-up no-limit poker M Moravcík, M Schmid, N Burch, V Lisy, D Morrill, N Bard, T Davis, ... Science 356 (6337), 508-513, 2017	565	2017
Regret minimization in games with incomplete information M Zinkevich, M Johanson, M Bowling, C Piccione Advances in Neural Information Processing Systems 20, 1729-1736, 2008	563	2008
Rational and convergent learning in stochastic games M Bowling, M Veloso International Joint Conference on Artificial Intelligence 17 (1), 1021-1026, 2001	389	2001
Heads-up limit hold’em poker is solved M Bowling, N Burch, M Johanson, O Tammelin Science 347 (6218), 145-149, 2015	381	2015
Convergence and no-regret in multiagent learning M Bowling Advances in neural information processing systems, 209-216, 2005	301	2005
Automatic gait optimization with gaussian process regression D Lizotte, T Wang, M Bowling, D Schuurmans Proc. of IJCAI, 944-949, 2007	277	2007
Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents MC Machado, MG Bellemare, E Talvitie, J Veness, M Hausknecht, ... Journal of Artificial Intelligence Research 61, 523-562, 2018	252	2018
Monte Carlo sampling for regret minimization in extensive games M Lanctot, K Waugh, M Zinkevich, M Bowling Advances in Neural Information Processing Systems 22, 1078-1086, 2009	218	2009
STP: Skills, tactics, and plays for multi-robot control in adversarial environments B Browning, J Bruce, M Bowling, M Veloso Proceedings of the Institution of Mechanical Engineers, Part I: Journal of …, 2005	216	2005
Bayes’ bluff: Opponent modelling in poker F Southey, M Bowling, B Larson, C Piccione, N Burch, D Billings, ... Proceedings of the 21st Annual Conference on Uncertainty in Artificial …, 2005	210*	2005
Apprenticeship learning using linear programming U Syed, M Bowling, RE Schapire Proceedings of the 25th international conference on Machine learning, 1032-1039, 2008	202	2008
Dyna-style planning with linear function approximation and prioritized sweeping RS Sutton, C Szepesvári, A Geramifard, MP Bowling arXiv preprint arXiv:1206.3285, 2012	169	2012
An analysis of stochastic game theory for multiagent reinforcement learning M Bowling, M Veloso Carnegie-Mellon Univ Pittsburgh Pa School of Computer Science, 2000	164	2000
A laplacian framework for option discovery in reinforcement learning MC Machado, MG Bellemare, M Bowling arXiv preprint arXiv:1703.00956, 2017	142	2017
Bayesian sparse sampling for on-line reward optimization T Wang, D Lizotte, M Bowling, D Schuurmans Proceedings of the 22nd international conference on Machine learning, 956-963, 2005	135	2005
Plays as Effective Multiagent Plans Enabling Opponent-Adaptive Play Selection. MH Bowling, B Browning, MM Veloso ICAPS, 376-383, 2004	125	2004
Solving heads-up limit Texas Hold'em O Tammelin, N Burch, M Johanson, M Bowling Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015	115	2015
Game-tree search with adaptation in stochastic imperfect-information games D Billings, A Davidson, T Schauenberg, N Burch, M Bowling, R Holte, ... International Conference on Computers and Games, 21-34, 2004	110	2004

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