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. PilarskiDeepMind, Amii, 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 MahmoodAmii, University of AlbertaVerified email at ualberta.ca
David SilverDeepMind, UCLVerified email at google.com
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
E James KehoeProfessor of Psychology, University of New South WalesVerified email at unsw.edu.au
David McAllesterProfessor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
Michael LittmanBrown UniversityVerified email at brown.edu
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


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	50717	2018
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3 (1), 9-44, 1988	6774	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	5411	1999
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	4502	1983
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	4492*	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	3310	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	1892	1990
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems 8, 1995	1659	1995
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1641	1981
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1990	1567	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	1237	2004
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	1029	1984
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1), 123-158, 1996	930	1996
Introduction to reinforcement learning. Vol. 135 RS Sutton, AG Barto MIT press Cambridge 5, 21-22, 1998	823	1998
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	789	1990
Dyna, an integrated architecture for learning, planning, and reacting RS Sutton ACM Sigart Bulletin 2 (4), 160-163, 1991	776	1991
Incremental natural actor-critic algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Advances in neural information processing systems, 2008	712*	2008
A menu of designs for reinforcement learning over time PJ Werbos, WT Miller, RS Sutton Neural networks for control 3, 67-95, 1990	687	1990
S., Barto A., G.,“ R Sutton Reinforcement Learning, An Introduction, 2000	670*	2000
Reinforcement learning is direct adaptive optimal control RS Sutton, AG Barto, RJ Williams IEEE control systems magazine 12 (2), 19-22, 1992	652	1992

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