Richard S. Sutton

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Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Satinder SinghComputer Science and Engineering, University of Michigan and DeepMindVerified 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 & 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
Adam WhiteUniversity of Alberta, DeepmindVerified email at ualberta.ca
Hamid MaeiCruise AutomationVerified email at getcruise.com
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
Martha WhiteUniversity of AlbertaVerified email at ualberta.ca
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	45009	2018
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3 (1), 9-44, 1988	6388	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	4485	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	4317	1983
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	3984*	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	2932	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	1790	1990
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1605	1981
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems, 1038-1044, 1996	1568	1996
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1990	1529	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	1247	2004
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	961	1984
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1), 123-158, 1996	891	1996
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	754	1990
A menu of designs for reinforcement learning over time PJ Werbos, WT Miller, RS Sutton Neural networks for control 3, 67-95, 1990	659	1990
Dyna, an integrated architecture for learning, planning, and reacting RS Sutton ACM Sigart Bulletin 2 (4), 160-163, 1991	640	1991
Incremental natural actor-critic algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Advances in neural information processing systems, 2008	629*	2008
Introduction to reinforcement learning. Vol. 135 RS Sutton, AG Barto MIT press Cambridge 10, 551283, 1998	623	1998
Predictive representations of state ML Littman, RS Sutton, S Singh Advances in neural information processing systems, 1555-1561, 2002	614	2002
Learning and sequential decision making AG Barto, RS Sutton, CJCH Watkins Learning and Computational Neuroscience: Foundations of Adaptive Networks, 1990	600	1990

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