Richard S. Sutton
Richard S. Sutton
DeepMind and Dept of Computing Science, University of Alberta
Verified email at - Homepage
TitleCited byYear
Reinforcement learning: An Introduction, 2nd edition
RS Sutton, AG Barto
MIT press, 2018
Reinforcement learning: An Introduction, 1st edition
RS Sutton, AG Barto
MIT press 135, 320, 1998
Learning to predict by the methods of temporal differences
RS Sutton
Machine learning 3 (1), 9-44, 1988
Neuronlike adaptive elements that can solve difficult learning control problems
AG Barto, RS Sutton, CW Anderson
IEEE transactions on systems, man, and cybernetics, 834-846, 1983
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
Policy gradient methods for reinforcement learning with function approximation
RS Sutton, DA McAllester, SP Singh, Y Mansour
Advances in neural information processing systems, 1057-1063, 2000
Toward a modern theory of adaptive networks: expectation and prediction.
RS Sutton, AG Barto
Psychological review 88 (2), 135, 1981
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
RS Sutton
Machine Learning Proceedings 1990, 216-224, 1990
Neural networks for control
WT Miller, PJ Werbos, RS Sutton
MIT press, 1995
Generalization in reinforcement learning: Successful examples using sparse coarse coding
RS Sutton
Advances in neural information processing systems, 1038-1044, 1996
Temporal credit assignment in reinforcement learning
RS Sutton
Reinforcement learning with replacing eligibility traces
SP Singh, RS Sutton
Machine learning 22 (1-3), 123-158, 1996
Time-derivative models of pavlovian reinforcement.
RS Sutton, AG Barto
The MIT Press, 1990
Learning and sequential decision making
AG Barto, RS Sutton, CJCH Watkins
Learning and computational neuroscience, 1989
Reinforcement learning for robocup soccer keepaway
P Stone, RS Sutton, G Kuhlmann
Adaptive Behavior 13 (3), 165-188, 2005
Predictive representations of state
ML Littman, RS Sutton, S Singh
Advances in neural information processing systems, 1555-1561, 2002
Reinforcement learning is direct adaptive optimal control
RS Sutton, AG Barto, RJ Williams
IEEE Control Systems 12 (2), 19-22, 1992
Experiments with reinforcement learning in problems with continuous state and action spaces
JC Santamaría, RS Sutton, A Ram
Adaptive behavior 6 (2), 163-217, 1997
Fast gradient-descent methods for temporal-difference learning with linear function approximation
RS Sutton, HR Maei, D Precup, S Bhatnagar, D Silver, C Szepesvári, ...
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
Landmark learning: An illustration of associative search
AG Barto, RS Sutton
Biological cybernetics 42 (1), 1-8, 1981
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