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

BETA: Related publications

Publications

You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect.

Dalrymple AN, Roszko DA, Sutton RS, et al. (2020) Pavlovian control of intraspinal microstimulation to produce over-ground walking. Journal of Neural Engineering
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
Mahmood AR, Sutton RS. (2015) Off-policy learning based on weighted importance sampling with linear computational complexity Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, Uai 2015. 552-561
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
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
Van Seijen H, Sutton RS. (2014) True online TD(λ) 31st International Conference On Machine Learning, Icml 2014. 2: 1048-1056
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
Van Hasselt H, Mahmood AR, Sutton RS. (2014) Off-policy TD(λ) with a true online equivalence Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, Uai 2014. 330-339
Yao H, Szepesvári C, Sutton R, et al. (2014) Universal option models Advances in Neural Information Processing Systems. 2: 990-998
See more...