Richard S. Sutton
Richard S. Sutton
DeepMind, Amii, and University of Alberta
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Reinforcement learning: An Introduction, 2nd edition
RS Sutton, AG Barto
MIT press, 2018
Learning to predict by the methods of temporal differences
RS Sutton
Machine learning 3 (1), 9-44, 1988
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
Neuronlike adaptive elements that can solve difficult learning control problems
AG Barto, RS Sutton, CW Anderson
IEEE transactions on systems, man, and cybernetics 13 (5), 834-846, 1983
Reinforcement learning: An Introduction, 1st edition
RS Sutton, AG Barto
MIT press, 1998
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
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
RS Sutton
Proceedings of the International Conference on Machine Learning, 216-224, 1990
Toward a modern theory of adaptive networks: Expectation and prediction.
RS Sutton, AG Barto
Psychological review 88 (2), 135, 1981
Generalization in reinforcement learning: Successful examples using sparse coarse coding
RS Sutton
Advances in neural information processing systems, 1038-1044, 1996
Neural networks for control
WT Miller, PJ Werbos, RS Sutton
MIT press, 1990
Guidelines on management (diagnosis and treatment) of syncope–update 2004: the Task Force on Syncope, European Society of Cardiology
M Brignole, P Alboni, DG Benditt, L Bergfeldt, JJ Blanc, PEB Thomsen, ...
European heart journal 25 (22), 2054-2072, 2004
Temporal credit assignment in reinforcement learning
RS Sutton
University of Massachusetts, Amherst, …, 1984
Reinforcement learning with replacing eligibility traces
SP Singh, RS Sutton
Machine learning 22 (1), 123-158, 1996
Time-derivative models of Pavlovian reinforcement.
RS Sutton, AG Barto
Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990
Introduction to reinforcement learning. Vol. 135
RS Sutton, AG Barto
MIT press Cambridge 10, 551283, 1998
Dyna, an integrated architecture for learning, planning, and reacting
RS Sutton
ACM Sigart Bulletin 2 (4), 160-163, 1991
Incremental natural actor-critic algorithms
S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee
Advances in neural information processing systems, 2008
A menu of designs for reinforcement learning over time
PJ Werbos, WT Miller, RS Sutton
Neural networks for control 3, 67-95, 1990
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 Magazine 12 (2), 19-22, 1992
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