Richard S. Sutton - Google Scholar Citations

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Satinder SinghComputer Science and Engineering, University of MichiganVerified email at umich.edu
Doina PrecupDeepMind and McGill UniversityVerified email at cs.mcgill.ca
Patrick M. PilarskiDeepMind and Dept. of Medicine, University of AlbertaVerified email at ualberta.ca
Charles Andersonprofessor of computer science, Colorado State University. Founder Pattern Exploration, LLC.Verified email at colostate.edu
Csaba SzepesvariDeepMind & Department of Computing Science, University of AlbertaVerified email at cs.ualberta.ca
Shalabh BhatnagarProfessor in the Department of Computer Science and Automation, Indian Institute of ScienceVerified email at iisc.ac.in
Hamid MaeiCruise AutomationVerified email at getcruise.com
Adam WhiteUniversity of Alberta, DeepmindVerified email at ualberta.ca
Thomas DegrisDeepMindVerified email at google.com
David SilverGoogle DeepMindVerified email at google.com
A. Rupam MahmoodUniversity of AlbertaVerified email at ualberta.ca
Joseph ModayilComputing Science, University of AlbertaVerified email at cs.ualberta.ca
Elliot A. LudvigProfessor, Psychology, University of WarwickVerified email at warwick.ac.uk
E James KehoeProfessor of Psychology, University of New South WalesVerified email at unsw.edu.au
Martha WhiteUniversity of AlbertaVerified email at ualberta.ca
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
David McAllesterProfessor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Mohammad GhavamzadehFacebook AI Research (FAIR)Verified email at inria.fr
Chris WatkinsProfessor in Computer Science, Royal HollowayVerified email at rhul.ac.uk

Richard S. Sutton

DeepMind and Dept of Computing Science, University of Alberta

Verified email at richsutton.com - Homepage

artificial intelligence reinforcement learning machine learning cognitive science computer science


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Reinforcement learning: An Introduction, 2nd edition RS Sutton, AG Barto MIT press, 2018	35988	2018
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	6779*	1998
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3 (1), 9-44, 1988	5658	1988
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	3912	1983
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	3076	2000
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	2356	1999
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1995	1640*	1995
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1565	1981
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	1550	1990
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems, 1038-1044, 1996	1399	1996
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	868	1984
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1-3), 123-158, 1996	801	1996
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	713	1990
A menu of designs for reinforcement learning over time PJ Werbos, WT Miller, RS Sutton Neural networks for control, 67-95, 1990	612	1990
S., Barto A., G.,“ R Sutton Reinforcement Learning, An Introduction, 2000	564*	2000
Learning and sequential decision making AG Barto, RS Sutton, CJCH Watkins Learning and Computational Neuroscience: Foundations of Adaptive Networks, 1990	557	1990
Reinforcement learning for robocup soccer keepaway P Stone, RS Sutton, G Kuhlmann Adaptive Behavior 13 (3), 165-188, 2005	501	2005
Predictive representations of state ML Littman, RS Sutton, S Singh Advances in neural information processing systems, 1555-1561, 2002	499	2002
Reinforcement learning is direct adaptive optimal control RS Sutton, AG Barto, RJ Williams IEEE Control Systems Magazine 12 (2), 19-22, 1992	483	1992
Dyna, an integrated architecture for learning, planning, and reacting RS Sutton ACM Sigart Bulletin 2 (4), 160-163, 1991	441	1991

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