Richard S. Sutton

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Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Satinder SinghDeepMind / U. of MichiganVerified email at umich.edu
Doina PrecupDeepMind and McGill UniversityVerified email at cs.mcgill.ca
Patrick M. PilarskiAmii and 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 & University of AlbertaVerified email at cs.ualberta.ca
Adam WhiteUniversity of Alberta, DeepmindVerified email at ualberta.ca
Shalabh BhatnagarProfessor in the Department of Computer Science and Automation, Indian Institute of ScienceVerified email at iisc.ac.in
Hamid MaeiNetflixVerified email at netflix.com
Thomas DegrisDeepMindVerified email at google.com
A. Rupam MahmoodUniversity of Alberta, AmiiVerified email at ualberta.ca
David SilverDeepMind, UCLVerified email at google.com
David McAllesterProfessor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Martha WhiteUniversity 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
Yishay MansourTel Aviv UniversityVerified email at tauex.tau.ac.il
E James KehoeProfessor of Psychology, University of New South WalesVerified email at unsw.edu.au
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
Michael LittmanBrown UniversityVerified email at brown.edu

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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Reinforcement learning: An Introduction, 2nd edition RS Sutton, AG Barto MIT press, 2018	57297	2018
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3, 9-44, 1988	7117	1988
Policy gradient methods for reinforcement learning with function approximation RS Sutton, D McAllester, S Singh, Y Mansour Advances in neural information processing systems 12, 1999	6242	1999
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	4887*	1998
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	4650	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	3675	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	1995	1990
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 Cardiology (ESC) A Moya, R Sutton European heart journal 30 (21), 2631-2671, 2009	1989	2009
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems 8, 1995	1741	1995
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1691	1981
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1990	1588	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	1323	2004
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	1089	1984
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1-3), 123-158, 1996	961	1996
Introduction to reinforcement learning. Vol. 135 RS Sutton, AG Barto MIT press Cambridge 5, 21-22, 1998	896	1998
Dyna, an integrated architecture for learning, planning, and reacting RS Sutton ACM Sigart Bulletin 2 (4), 160-163, 1991	875	1991
Between MDPs and Semi-MDPs: Learning, planning, and representing knowledge at multiple temporal scales RS Sutton	806	1998
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	790	1990
Tilt table testing for assessing syncope DG Benditt, DW Ferguson, BP Grubb, WN Kapoor, J Kugler, BB Lerman, ... Journal of the American College of Cardiology 28 (1), 263-275, 1996	757	1996
Eligibility traces for off-policy policy evaluation D Precup, RS Sutton, S Singh International Conference on Machine Learning 16, 759-766, 2000	732	2000

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