| 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 | 1741 | 2013 |
| Multiagent learning using a variable learning rate M Bowling, M Veloso Artificial Intelligence 136 (2), 215-250, 2002 | 865 | 2002 |
| 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 | 548 | 2008 |
| 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 | 526 | 2017 |
| Rational and convergent learning in stochastic games M Bowling, M Veloso International Joint Conference on Artificial Intelligence 17 (1), 1021-1026, 2001 | 384 | 2001 |
| Heads-up limit hold’em poker is solved M Bowling, N Burch, M Johanson, O Tammelin Science 347 (6218), 145-149, 2015 | 365 | 2015 |
| Convergence and no-regret in multiagent learning M Bowling Advances in neural information processing systems, 209-216, 2005 | 300 | 2005 |
| Automatic gait optimization with gaussian process regression D Lizotte, T Wang, M Bowling, D Schuurmans Proc. of IJCAI, 944-949, 2007 | 273 | 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 | 237 | 2018 |
| 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 | 215 | 2005 |
| 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 | 214 | 2009 |
| 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 | 205* | 2005 |
| Apprenticeship learning using linear programming U Syed, M Bowling, RE Schapire Proceedings of the 25th international conference on Machine learning, 1032-1039, 2008 | 194 | 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 | 166 | 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 | 158 | 2000 |
| A laplacian framework for option discovery in reinforcement learning MC Machado, MG Bellemare, M Bowling arXiv preprint arXiv:1703.00956, 2017 | 138 | 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 | 132 | 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 | 111 | 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 |
Manuela M. VelosoHerbert A. Simon University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
