Michael Bowling
Cited by
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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
Multiagent learning using a variable learning rate
M Bowling, M Veloso
Artificial Intelligence 136 (2), 215-250, 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
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
Rational and convergent learning in stochastic games
M Bowling, M Veloso
International Joint Conference on Artificial Intelligence 17 (1), 1021-1026, 2001
Heads-up limit hold’em poker is solved
M Bowling, N Burch, M Johanson, O Tammelin
Science 347 (6218), 145-149, 2015
Convergence and no-regret in multiagent learning
M Bowling
Advances in neural information processing systems, 209-216, 2005
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
Automatic gait optimization with gaussian process regression
D Lizotte, T Wang, M Bowling, D Schuurmans
Proc. of IJCAI, 944-949, 2007
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
Apprenticeship learning using linear programming
U Syed, M Bowling, RE Schapire
Proceedings of the 25th international conference on Machine learning, 1032-1039, 2008
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
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
Dyna-style planning with linear function approximation and prioritized sweeping
RS Sutton, C Szepesvári, A Geramifard, MP Bowling
arXiv preprint arXiv:1206.3285, 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
A laplacian framework for option discovery in reinforcement learning
MC Machado, MG Bellemare, M Bowling
arXiv preprint arXiv:1703.00956, 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
Solving heads-up limit Texas Hold'em
O Tammelin, N Burch, M Johanson, M Bowling
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
Plays as Effective Multiagent Plans Enabling Opponent-Adaptive Play Selection.
MH Bowling, B Browning, MM Veloso
ICAPS, 376-383, 2004
The Hanabi challenge: A new frontier for AI research
N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ...
Artificial Intelligence 280, 103216, 2020
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