| Policy Gradient Methods for Reinforcement Learning with Function Approximation R Sutton, D McAllester, S Singh, Y Mansour Neural Information Processing Systems, 1999 | 10512 | 1999 |
| Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning RS Sutton, D Precup, S Singh Artificial intelligence 112 (1-2), 181-211, 1999 | 5246 | 1999 |
| Learning to act using real-time dynamic programming AG Barto, SJ Bradtke, SP Singh Artificial intelligence 72 (1-2), 81-138, 1995 | 1758 | 1995 |
| Near-optimal reinforcement learning in polynomial time M Kearns, S Singh Machine learning 49 (2), 209-232, 2002 | 1530 | 2002 |
| Convergence of stochastic iterative dynamic programming algorithms T Jaakkola, M Jordan, S Singh Advances in neural information processing systems 6, 1993 | 1495 | 1993 |
| Action-conditional video prediction using deep networks in atari games J Oh, X Guo, H Lee, RL Lewis, S Singh Advances in neural information processing systems 28, 2015 | 1179 | 2015 |
| Intrinsically motivated reinforcement learning N Chentanez, A Barto, S Singh Advances in neural information processing systems 17, 2004 | 1148 | 2004 |
| Eligibility traces for off-policy policy evaluation D Precup, R Sutton, S Singh Computer Science Department Faculty Publication Series, 80, 2000 | 1135 | 2000 |
| Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1), 123-158, 1996 | 1126 | 1996 |
| Convergence results for single-step on-policy reinforcement-learning algorithms S Singh, T Jaakkola, ML Littman, C Szepesvári Machine learning 38 (3), 287-308, 2000 | 1122 | 2000 |
| Reward is enough D Silver, S Singh, D Precup, RS Sutton Artificial intelligence 299, 103535, 2021 | 889 | 2021 |
| Graphical models for game theory M Kearns, ML Littman, S Singh arXiv preprint arXiv:1301.2281, 2013 | 866 | 2013 |
| Predictive representations of state ML Littman, RS Sutton, S Singh Advances in neural information processing systems, 1555-1561, 2002 | 773 | 2002 |
| Learning without state-estimation in partially observable Markovian decision processes SP Singh, T Jaakkola, MI Jordan Machine Learning Proceedings 1994, 284-292, 1994 | 659 | 1994 |
| Intrinsically motivated reinforcement learning: An evolutionary perspective S Singh, RL Lewis, AG Barto, J Sorg IEEE Transactions on Autonomous Mental Development 2 (2), 70-82, 2010 | 640 | 2010 |
| Intrinsically motivated learning of hierarchical collections of skills AG Barto, S Singh, N Chentanez Proceedings of the 3rd International Conference on Development and Learning …, 2004 | 616 | 2004 |
| Optimizing dialogue management with reinforcement learning: Experiments with the NJFun system S Singh, D Litman, M Kearns, M Walker Journal of Artificial Intelligence Research 16, 105-133, 2002 | 532 | 2002 |
| Transfer of learning by composing solutions of elemental sequential tasks SP Singh Machine learning 8 (3), 323-339, 1992 | 520 | 1992 |
| Reinforcement Learning with Soft State Aggregation S Singh, T Jaakkola, M Jordan Neural Information Processing Systems, 1995 | 502 | 1995 |
| Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning X Guo, S Singh, H Lee, R Lewis, X Wang Advances in neural information processing systems 27, 2014 | 489 | 2014 |
Richard S. SuttonKeen, Amii, and University of AlbertaVerified email at richsutton.com
