Richard S. Sutton
|University of Alberta, Edmonton, Alberta, Canada|
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)
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|Dalrymple AN, Roszko DA, Sutton RS, et al. (2020) Pavlovian control of intraspinal microstimulation to produce over-ground walking. Journal of Neural Engineering|
|Travnik JB, Mathewson KW, Sutton RS, et al. (2018) Reactive Reinforcement Learning in Asynchronous Environments. Frontiers in Robotics and Ai. 5: 79|
|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|
|Yao H, Szepesvári C, Sutton R, et al. (2014) Universal option models Advances in Neural Information Processing Systems. 2: 990-998|
|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|
|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 Seijen H, Sutton RS. (2014) True online TD(λ) 31st International Conference On Machine Learning, Icml 2014. 2: 1048-1056|