Richard S. Sutton

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Andrew BartoUniversity of Massachusetts AmherstEmail yang diverifikasi di cs.umass.edu
Satinder SinghGoogle DeepMind / U. of MichiganEmail yang diverifikasi di umich.edu
Doina PrecupDeepMind and McGill UniversityEmail yang diverifikasi di cs.mcgill.ca
Patrick M. PilarskiProfessor, University of Alberta, Amii (Alberta Machine Intelligence Institute)Email yang diverifikasi di ualberta.ca
Charles Andersonprofessor of computer science, Colorado State University. Founder Pattern Exploration, LLC.Email yang diverifikasi di colostate.edu
A. Rupam MahmoodUniversity of Alberta, AmiiEmail yang diverifikasi di ualberta.ca
Adam WhiteUniversity of Alberta, Amii (Alberta Machine Intelligence Institute)Email yang diverifikasi di ualberta.ca
Csaba SzepesvariDeepMind & University of AlbertaEmail yang diverifikasi di cs.ualberta.ca
Shalabh BhatnagarProfessor in the Department of Computer Science and Automation, Indian Institute of ScienceEmail yang diverifikasi di iisc.ac.in
Thomas DegrisDeepMindEmail yang diverifikasi di google.com
Hamid MaeiNetflixEmail yang diverifikasi di netflix.com
David SilverDeepMind, UCLEmail yang diverifikasi di google.com
Martha WhiteUniversity of AlbertaEmail yang diverifikasi di ualberta.ca
David McAllesterProfessor, Toyota Technological Institute at ChicagoEmail yang diverifikasi di ttic.edu
Joseph ModayilOpenmind Research Institute & Keen AGIEmail yang diverifikasi di openmindresearch.org
Yishay MansourTel Aviv UniversityEmail yang diverifikasi di tauex.tau.ac.il
Elliot A. LudvigProfessor, Psychology, University of WarwickEmail yang diverifikasi di warwick.ac.uk
E James KehoeProfessor of Psychology, University of New South WalesEmail yang diverifikasi di unsw.edu.au
Peter StoneProfessor of Computer Science, The University of Texas at AustinEmail yang diverifikasi di cs.utexas.edu
Mohammad GhavamzadehAmazonEmail yang diverifikasi di amazon.com

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Richard S. Sutton

Keen, Amii, and University of Alberta

Email yang diverifikasi di richsutton.com - Beranda

artificial intelligence reinforcement learning machine learning cognitive science computer science


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Reinforcement learning: An Introduction, 2nd edition RS Sutton, AG Barto MIT press, 2018	77011	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	8931	1999
Learning to predict by the methods of temporal differences RS Sutton Machine learning 3, 9-44, 1988	8255	1988
Reinforcement learning: An Introduction, 1st edition RS Sutton, AG Barto MIT press, 1998	6066*	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	5226	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	4620	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	2281	1990
Generalization in reinforcement learning: Successful examples using sparse coarse coding RS Sutton Advances in neural information processing systems 8, 1995	1903	1995
Toward a modern theory of adaptive networks: Expectation and prediction. RS Sutton, AG Barto Psychological review 88 (2), 135, 1981	1861	1981
Neural networks for control WT Miller, PJ Werbos, RS Sutton MIT press, 1990	1849	1990
Temporal credit assignment in reinforcement learning RS Sutton University of Massachusetts, Amherst, http://www.incompleteideas.net/papers …, 1984	1251	1984
Dyna, an integrated architecture for learning, planning, and reacting RS Sutton ACM Sigart Bulletin 2 (4), 160-163, 1991	1247	1991
Introduction to reinforcement learning. Vol. 135 RS Sutton, AG Barto MIT press Cambridge 5, 21-22, 1998	1175	1998
Incremental natural actor-critic algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Advances in neural information processing systems, 2008	1108	2008
Reinforcement learning with replacing eligibility traces SP Singh, RS Sutton Machine learning 22 (1), 123-158, 1996	1055	1996
Eligibility traces for off-policy policy evaluation D Precup, RS Sutton, S Singh International Conference on Machine Learning 16, 759-766, 2000	999	2000
Time-derivative models of Pavlovian reinforcement. RS Sutton, AG Barto Learning and Computational Neuroscience: Foundations of Adaptive Networks …, 1990	853	1990
Reinforcement learning is direct adaptive optimal control RS Sutton, AG Barto, RJ Williams IEEE control systems magazine 12 (2), 19-22, 1992	846	1992
A menu of designs for reinforcement learning over time WT Miller, RS Sutton, PJ Werbos MIT press, 1995	776	1995
S., Barto A., G.,“ R Sutton Reinforcement Learning, An Introduction, 2000	742*	2000

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