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. PilarskiProfessor, University 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
A. Rupam MahmoodUniversity of Alberta, AmiiVerified email at ualberta.ca
Adam WhiteUniversity of Alberta, Amii (Alberta Machine Intelligence Institute)Verified email at ualberta.ca
Csaba SzepesvariDeepMind & 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
Thomas DegrisDeepMindVerified email at google.com
Hamid MaeiNetflixVerified email at netflix.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 ModayilOpenmind Research Institute & Keen AGIVerified email at openmindresearch.org
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
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
Mohammad GhavamzadehAmazon AGIVerified email at amazon.com

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	79588*	2018
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	9255	1999
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3, 9-44, 1988	8391	1988
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	6175*	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	5304	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	4729	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	2310	1990
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems 8, 1995	1920	1995
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1878	1981
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1990	1856	1990
Dyna, an integrated architecture for learning, planning, and reacting RS Sutton ACM Sigart Bulletin 2 (4), 160-163, 1991	1296	1991
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	1266	1984
Introduction to reinforcement learning. Vol. 135 RS Sutton, AG Barto MIT press Cambridge 5, 21-22, 1998	1195	1998
Incremental natural actor-critic algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Advances in neural information processing systems, 2008	1119	2008
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1), 123-158, 1996	1066	1996
Eligibility traces for off-policy policy evaluation D Precup, RS Sutton, S Singh International Conference on Machine Learning 16, 759-766, 2000	1022	2000
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	860	1990
Reinforcement learning is direct adaptive optimal control RS Sutton, AG Barto, RJ Williams IEEE control systems magazine 12 (2), 19-22, 1992	859	1992
A menu of designs for reinforcement learning over time WT Miller, RS Sutton, PJ Werbos MIT press, 1995	784	1995
Fast gradient-descent methods for temporal-difference learning with linear function approximation RS Sutton, HR Maei, D Precup, S Bhatnagar, D Silver, C Szepesvári, ... Proceedings of the 26th annual international conference on machine learning …, 2009	752	2009

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