Richard S. Sutton

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Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Satinder SinghGoogle DeepMind / U. of MichiganVerified email at umich.edu
Doina PrecupDeepMind and McGill UniversityVerified email at cs.mcgill.ca
Patrick M. PilarskiUniversity of Alberta, Amii (Alberta Machine Intelligence Institute)Verified 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, Amii (Alberta Machine Intelligence Institute)Verified email at ualberta.ca
Shalabh BhatnagarProfessor in the Department of Computer Science and Automation, Indian Institute of ScienceVerified email at iisc.ac.in
A. Rupam MahmoodUniversity of Alberta, AmiiVerified email at ualberta.ca
Hamid MaeiNetflixVerified email at netflix.com
Thomas DegrisDeepMindVerified email at google.com
David SilverDeepMind, UCLVerified email at google.com
Martha WhiteUniversity of AlbertaVerified email at ualberta.ca
David McAllesterProfessor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Joseph ModayilDeepMindVerified email at cs.ualberta.ca
Yishay MansourTel Aviv UniversityVerified email at tauex.tau.ac.il
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
Mohammad GhavamzadehAmazonVerified email at amazon.com
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu

Richard S. Sutton

Keen, 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	66092	2018
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3, 9-44, 1988	7623	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	7541	1999
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	5524*	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	4901	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	4106	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	2127	1990
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems 8, 1995	1824	1995
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1788	1981
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1990	1759	1990
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	1174	1984
Dyna, an integrated architecture for learning, planning, and reacting RS Sutton ACM Sigart Bulletin 2 (4), 160-163, 1991	1030	1991
Introduction to reinforcement learning. Vol. 135 RS Sutton, AG Barto MIT press Cambridge 5, 21-22, 1998	1013	1998
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22, 123-158, 1996	1010	1996
Eligibility traces for off-policy policy evaluation D Precup, RS Sutton, S Singh International Conference on Machine Learning 16, 759-766, 2000	863	2000
Natural actor-critic algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Automatica 7 (8), 12, 2009	843	2009
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	824	1990
Reinforcement learning is direct adaptive optimal control RS Sutton, AG Barto, RJ Williams IEEE control systems magazine 12 (2), 19-22, 1992	788	1992
A menu of designs for reinforcement learning over time WT Miller, RS Sutton, PJ Werbos MIT press, 1995	752	1995
Reinforcement learning: An introduction (adaptive computation and machine learning) AG Barto, RS Sutton MIT press, 1998	742	1998

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