Richard S. Sutton

Patrick M. Pilarski, Michael Rory Dawson, Thomas Degris, Jason P. Carey, K. Ming Chan, Jacqueline S. Hebert, Richard S. Sutton: Adaptive Artificial Limbs: A Real-Time Approach to Prediction and Anticipation. IEEE Robot. Automat. Mag. 20(1): 53-64 (2013)

Harm van Seijen, Richard S. Sutton: Planning by Prioritized Sweeping with Small Backups. CoRR abs/1301.2343 (2013)

David Silver, Richard S. Sutton, Martin Müller: Temporal-difference search in computer Go. Machine Learning 87(2): 183-219 (2012)

Ashique Rupam Mahmood, Richard S. Sutton, Thomas Degris, Patrick M. Pilarski: Tuning-free step-size adaptation. ICASSP 2012: 2121-2124

Adam White, Joseph Modayil, Richard S. Sutton: Scaling life-long off-policy learning. ICDL-EPIROB 2012: 1-6

Thomas Degris, Martha White, Richard S. Sutton: Linear Off-Policy Actor-Critic. ICML 2012

Richard S. Sutton: Beyond Reward: The Problem of Knowledge and Data. ILP 2012: 2-6

Joseph Modayil, Adam White, Richard S. Sutton: Multi-timescale Nexting in a Reinforcement Learning Robot. SAB 2012: 299-309

Joseph Modayil, Adam White, Patrick M. Pilarski, Richard S. Sutton: Acquiring a broad range of empirical knowledge in real time by temporal-difference learning. SMC 2012: 1903-1910

Thomas Degris, Martha White, Richard S. Sutton: Off-Policy Actor-Critic. CoRR abs/1205.4839 (2012)

Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael Bowling: Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping. CoRR abs/1206.3285 (2012)

Adam White, Joseph Modayil, Richard S. Sutton: Scaling Life-long Off-policy Learning. CoRR abs/1206.6262 (2012)

Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, Doina Precup: Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. AAMAS 2011: 761-768

Joseph Modayil, Adam White, Richard S. Sutton: Multi-timescale Nexting in a Reinforcement Learning Robot. CoRR abs/1112.1133 (2011)

Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Richard S. Sutton: Toward Off-Policy Learning Control with Function Approximation. ICML 2010: 719-726

Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee: Natural actor-critic algorithms. Automatica 45(11): 2471-2482 (2009)

Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora: Fast gradient-descent methods for temporal-difference learning with linear function approximation. ICML 2009: 125

Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. NIPS 2009: 1204-1212

Hengshuai Yao, Richard S. Sutton, Shalabh Bhatnagar, Diao Dongcui, Csaba Szepesvári: Multi-Step Dyna Planning for Policy Evaluation and Control. NIPS 2009: 2187-2195

Elliot A. Ludvig, Richard S. Sutton, E. James Kehoe: Stimulus Representation and the Timing of Reward-Prediction Errors in Models of the Dopamine System. Neural Computation 20(12): 3034-3054 (2008)

Maria Cutumisu, Duane Szafron, Michael H. Bowling, Richard S. Sutton: Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games. AIIDE 2008

David Silver, Richard S. Sutton, Martin Müller: Sample-based learning and search with permanent and transient memories. ICML 2008: 968-975

Elliot A. Ludvig, Richard S. Sutton, Eric Verbeek, E. James Kehoe: A computational model of hippocampal function in trace conditioning. NIPS 2008: 993-1000

Richard S. Sutton, Csaba Szepesvári, Hamid Reza Maei: A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation. NIPS 2008: 1609-1616

Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael H. Bowling: Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping. UAI 2008: 528-536

Richard S. Sutton, Anna Koop, David Silver: On the role of tracking in stationary environments. ICML 2007: 871-878

David Silver, Richard S. Sutton, Martin Müller: Reinforcement Learning of Local Shape in the Game of Go. IJCAI 2007: 1053-1058

Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee: Incremental Natural Actor-Critic Algorithms. NIPS 2007

Alborz Geramifard, Michael H. Bowling, Richard S. Sutton: Incremental Least-Squares Temporal Difference Learning. AAAI 2006: 356-361

Alborz Geramifard, Michael H. Bowling, Martin Zinkevich, Richard S. Sutton: iLSTD: Eligibility Traces and Convergence Analysis. NIPS 2006: 441-448

Brian Tanner, Richard S. Sutton: TD(lambda) networks: temporal-difference networks with eligibility traces. ICML 2005: 888-895

Eddie J. Rafols, Mark B. Ring, Richard S. Sutton, Brian Tanner: Using Predictive Representations to Improve Generalization in Reinforcement Learning. IJCAI 2005: 835-840

Brian Tanner, Richard S. Sutton: Temporal-Difference Networks with History. IJCAI 2005: 865-870

Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder P. Singh: Off-policy Learning with Options and Recognizers. NIPS 2005

Richard S. Sutton, Eddie J. Rafols, Anna Koop: Temporal Abstraction in Temporal-difference Networks. NIPS 2005

Richard S. Sutton, Brian Tanner: Temporal-Difference Networks. NIPS 2004

Rina Dechter, Richard S. Sutton (Eds.): Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28 - August 1, 2002, Edmonton, Alberta, Canada. AAAI Press / The MIT Press 2002

Doina Precup, Richard S. Sutton, Sanjoy Dasgupta: Off-Policy Temporal Difference Learning with Function Approximation. ICML 2001: 417-424

Peter Stone, Richard S. Sutton: Scaling Reinforcement Learning toward RoboCup Soccer. ICML 2001: 537-544

Michael L. Littman, Richard S. Sutton, Satinder P. Singh: Predictive Representations of State. NIPS 2001: 1555-1561

Peter Stone, Richard S. Sutton: Keepaway Soccer: A Machine Learning Testbed. RoboCup 2001: 214-223

Doina Precup, Richard S. Sutton, Satinder P. Singh: Eligibility Traces for Off-Policy Policy Evaluation. ICML 2000: 759-766

Peter Stone, Richard S. Sutton, Satinder P. Singh: Reinforcement Learning for 3 vs. 2 Keepaway. RoboCup 2000: 249-258

Richard S. Sutton, Doina Precup, Satinder P. Singh: Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artif. Intell. 112(1-2): 181-211 (1999)

Richard S. Sutton: Open Theoretical Questions in Reinforcement Learning. EuroCOLT 1999: 11-17

Richard S. Sutton, David A. McAllester, Satinder P. Singh, Yishay Mansour: Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS 1999: 1057-1063

Richard S. Sutton, Andrew G. Barto: Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks 9(5): 1054-1054 (1998)

Doina Precup, Richard S. Sutton, Satinder P. Singh: Theoretical Results on Reinforcement Learning with Temporally Abstract Options. ECML 1998: 382-393

Richard S. Sutton, Doina Precup, Satinder P. Singh: Intra-Option Learning about Temporally Abstract Actions. ICML 1998: 556-564

Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton: Learning Instance-Independent Value Functions to Enhance Local Search. NIPS 1998: 1017-1023

Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran: Improved Switching among Temporally Abstract Actions. NIPS 1998: 1066-1072

Richard S. Sutton: Reinforcement Learning: Past, Present and Future. SEAL 1998: 195-197

Richard S. Sutton: On the Significance of Markov Decision Processes. ICANN 1997: 273-282

Doina Precup, Richard S. Sutton: Exponentiated Gradient Methods for Reinforcement Learning. ICML 1997: 272-277

Doina Precup, Richard S. Sutton: Multi-time Models for Temporally Abstract Planning. NIPS 1997

Satinder P. Singh, Richard S. Sutton: Reinforcement Learning with Replacing Eligibility Traces. Machine Learning 22(1-3): 123-158 (1996)

Richard S. Sutton: TD Models: Modeling the World at a Mixture of Time Scales. ICML 1995: 531-539

Richard S. Sutton: Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. NIPS 1995: 1038-1044

Richard S. Sutton, Steven D. Whitehead: Online Learning with Random Representations. ICML 1993: 314-321

Richard S. Sutton: Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta. AAAI 1992: 171-176

Richard S. Sutton: Dyna, an Integrated Architecture for Learning, Planning, and Reacting. SIGART Bulletin 2(4): 160-163 (1991)

Richard S. Sutton, Christopher J. Matheus: Learning Polynomial Functions by Feature Construction. ML 1991: 208-212

Richard S. Sutton: Planning by Incremental Dynamic Programming. ML 1991: 353-357

Terence D. Sanger, Richard S. Sutton, Christopher J. Matheus: Iterative Construction of Sparse Polynomial Approximations. NIPS 1991: 1064-1071

Richard S. Sutton: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. ML 1990: 216-224

Richard S. Sutton: Integrated Modeling and Control Based on Reinforcement Learning. NIPS 1990: 471-478

Andrew G. Barto, Richard S. Sutton, Christopher J. C. H. Watkins: Sequential Decision Probelms and Neural Networks. NIPS 1989: 686-693

Richard S. Sutton: Learning to Predict by the Methods of Temporal Differences. Machine Learning 3: 9-44 (1988)

Oliver G. Selfridge, Richard S. Sutton, Andrew G. Barto: Training and Tracking in Robotics. IJCAI 1985: 670-672