Richard S. Sutton - Google Scholar

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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
Hamid MaeiCruise AutomationVerified email at getcruise.com
Shalabh BhatnagarProfessor in the Department of Computer Science and Automation, Indian Institute of ScienceVerified email at iisc.ac.in
Adam WhiteUniversity of Alberta, DeepmindVerified email at ualberta.ca
Thomas DegrisDeepMindVerified email at google.com
David SilverGoogle DeepMindVerified email at google.com
A. Rupam MahmoodAmii, University 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 GhavamzadehGoogle ResearchVerified email at inria.fr
Chris WatkinsProfessor in Computer Science, Royal HollowayVerified email at rhul.ac.uk

Richard S. Sutton

DeepMind, Amii, and 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	38284	2018
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	7070*	1998
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3 (1), 9-44, 1988	5854	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	3978	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	3489	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	2513	1999
Guidelines: Guidelines for the diagnosis and management of syncope (version 2009): The Task Force for the Diagnosis and Management of Syncope of the European Society of … A Moya, R Sutton, F Ammirati, JJ Blanc, M Brignole, JB Dahm, JC Deharo, ... European heart journal 30 (21), 2631, 2009	1810	2009
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1995	1665*	1995
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	1619	1990
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1593	1981
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems, 1038-1044, 1996	1454	1996
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	886	1984
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1-3), 123-158, 1996	818	1996
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	712	1990
A menu of designs for reinforcement learning over time PJ Werbos, WT Miller, RS Sutton Neural networks for control, 67-95, 1990	624	1990
2018 ESC Guidelines for the diagnosis and management of syncope M Brignole, A Moya, FJ de Lange, JC Deharo, PM Elliott, A Fanciulli, ... European heart journal 39 (21), 1883-1948, 2018	590	2018
S., Barto A., G.,“ R Sutton Reinforcement Learning, An Introduction, 2000	589*	2000
Learning and sequential decision making AG Barto, RS Sutton, CJCH Watkins Learning and Computational Neuroscience: Foundations of Adaptive Networks, 1990	563	1990
Natural actor-critic algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Automatica 7 (8), 12, 2009	545*	2009
Incremental natural actor-critic algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Advances in neural information processing systems, 2008	545	2008

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