Richard S. Sutton

Affiliations: 
University of Alberta, Edmonton, Alberta, Canada 
Area:
Reinforcement Learning
Website:
http://www.cs.ualberta.ca/~sutton/index.html
Google:
"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)
 
Cross-listing: MathTree

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Publications

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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
Mahmood AR, Van Hasselt H, Sutton RS. (2014) Weighted importance sampling for off-policy learning with linear function approximation Advances in Neural Information Processing Systems. 4: 3014-3022
Sutton RS, Mahmood AR, Precup D, et al. (2014) A new Q(λ) with interim forward view and Monte Carlo equivalence 31st International Conference On Machine Learning, Icml 2014. 3: 1973-1988
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