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Richard S. Sutton

Affiliations: 
University of Alberta, Edmonton, Alberta, Canada 
Area:
Reinforcement Learning
Website:
http://www.cs.ualberta.ca/~sutton/index.html
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"Richard Sutton"
Bio:

Richard S. Sutton is a professor and iCORE chair in the department of computing science at the University of Alberta. He is a fellow of the American Association for Artificial Intelligence and co-author of the textbook Reinforcement Learning: An Introduction from MIT Press. Before joining the University of Alberta in 2003, he worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. He received a PhD in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978. Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence. He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.

Mean distance: 14.24 (cluster 29)
 		SNBCP
Cross-listing: MathTree

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Parents

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Andrew G. Barto grad student U Mass Amherst

Children

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Doina Precup grad student 2000 U Mass Amherst
David Silver grad student 2009 University of Alberta
Hamid Reza Maei grad student 2007-2011 University of Alberta
Elliot A. Ludvig post-doc 2006-2010 University of Alberta

Collaborators

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Ashley N Dalrymple collaborator 2013-2018 University of Alberta
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Publications

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Dohare S, Hernandez-Garcia JF, Lan Q, et al. (2024) Loss of plasticity in deep continual learning. Nature. 632: 768-774
Ghiassian S, Rafiee B, Sutton RS. (2024) Off-Policy Prediction Learning: An Empirical Study of Online Algorithms. Ieee Transactions On Neural Networks and Learning Systems
Rafiee B, Abbas Z, Ghiassian S, et al. (2022) From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. Adaptive Behavior. 31: 3-19
Dalrymple AN, Roszko DA, Sutton RS, et al. (2020) Pavlovian control of intraspinal microstimulation to produce over-ground walking. Journal of Neural Engineering
De Asis K, Chan A, Pitis S, et al. (2020) Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning Proceedings of the Aaai Conference On Artificial Intelligence. 34: 3741-3748
Travnik JB, Mathewson KW, Sutton RS, et al. (2018) Reactive Reinforcement Learning in Asynchronous Environments. Frontiers in Robotics and Ai. 5: 79
Travnik JB, Mathewson KW, Sutton RS, et al. (2018) Reactive Reinforcement Learning in Asynchronous Environments Frontiers in Robotics and Ai. 5
Edwards AL, Dawson MR, Hebert JS, et al. (2015) Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching. Prosthetics and Orthotics International
Kehoe EJ, Ludvig EA, Sutton RS. (2014) Time course of the rabbit's conditioned nictitating membrane movements during acquisition, extinction, and reacquisition. Learning & Memory (Cold Spring Harbor, N.Y.). 21: 585-90
Modayil J, White A, Sutton RS. (2014) Multi-timescale nexting in a reinforcement learning robot Adaptive Behavior. 22: 146-160
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