| Year |
Citation |
Score |
| 2024 |
Dohare S, Hernandez-Garcia JF, Lan Q, Rahman P, Mahmood AR, Sutton RS. Loss of plasticity in deep continual learning. Nature. 632: 768-774. PMID 39169245 DOI: 10.1038/s41586-024-07711-7 |
0.413 |
|
| 2024 |
Ghiassian S, Rafiee B, Sutton RS. Off-Policy Prediction Learning: An Empirical Study of Online Algorithms. Ieee Transactions On Neural Networks and Learning Systems. PMID 38857133 DOI: 10.1109/TNNLS.2024.3373749 |
0.333 |
|
| 2022 |
Rafiee B, Abbas Z, Ghiassian S, Kumaraswamy R, Sutton RS, Ludvig EA, White A. From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. Adaptive Behavior. 31: 3-19. PMID 36618906 DOI: 10.1177/10597123221085039 |
0.717 |
|
| 2020 |
Dalrymple AN, Roszko DA, Sutton RS, Mushahwar VK. Pavlovian control of intraspinal microstimulation to produce over-ground walking. Journal of Neural Engineering. PMID 32348970 DOI: 10.1088/1741-2552/Ab8E8E |
0.671 |
|
| 2020 |
De Asis K, Chan A, Pitis S, Sutton R, Graves D. Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning Proceedings of the Aaai Conference On Artificial Intelligence. 34: 3741-3748. DOI: 10.1609/aaai.v34i04.5784 |
0.414 |
|
| 2018 |
Travnik JB, Mathewson KW, Sutton RS, Pilarski PM. Reactive Reinforcement Learning in Asynchronous Environments. Frontiers in Robotics and Ai. 5: 79. PMID 33500958 DOI: 10.3389/frobt.2018.00079 |
0.316 |
|
| 2018 |
Travnik JB, Mathewson KW, Sutton RS, Pilarski PM. Reactive Reinforcement Learning in Asynchronous Environments Frontiers in Robotics and Ai. 5. DOI: 10.3389/frobt.2018.00079 |
0.437 |
|
| 2015 |
Edwards AL, Dawson MR, Hebert JS, Sherstan C, Sutton RS, Chan KM, Pilarski PM. Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching. Prosthetics and Orthotics International. PMID 26423106 DOI: 10.1177/0309364615605373 |
0.44 |
|
| 2014 |
Kehoe EJ, Ludvig EA, Sutton RS. 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. PMID 25320350 DOI: 10.1101/Lm.034504.114 |
0.645 |
|
| 2014 |
Modayil J, White A, Sutton RS. Multi-timescale nexting in a reinforcement learning robot Adaptive Behavior. 22: 146-160. DOI: 10.1177/1059712313511648 |
0.465 |
|
| 2014 |
Mahmood AR, Van Hasselt H, Sutton RS. Weighted importance sampling for off-policy learning with linear function approximation Advances in Neural Information Processing Systems. 4: 3014-3022. |
0.331 |
|
| 2014 |
Sutton RS, Mahmood AR, Precup D, Van Hasselt H. A new Q(λ) with interim forward view and Monte Carlo equivalence 31st International Conference On Machine Learning, Icml 2014. 3: 1973-1988. |
0.447 |
|
| 2013 |
Pilarski PM, Dick TB, Sutton RS. Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints. Ieee ... International Conference On Rehabilitation Robotics : [Proceedings]. 2013: 6650435. PMID 24187253 DOI: 10.1109/ICORR.2013.6650435 |
0.391 |
|
| 2013 |
Kehoe EJ, Ludvig EA, Sutton RS. Timing and cue competition in conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus). Learning & Memory (Cold Spring Harbor, N.Y.). 20: 97-102. PMID 23325726 DOI: 10.1101/Lm.028183.112 |
0.635 |
|
| 2013 |
Pilarski PM, Dawson MR, Degris T, Carey J, Chan KM, Hebert JS, Sutton RS. Adaptive artificial limbs: A real-time approach to prediction and anticipation Ieee Robotics and Automation Magazine. 20: 53-64. DOI: 10.1109/MRA.2012.2229948 |
0.374 |
|
| 2012 |
Ludvig EA, Sutton RS, Kehoe EJ. Evaluating the TD model of classical conditioning. Learning & Behavior. 40: 305-19. PMID 22927003 DOI: 10.3758/S13420-012-0082-6 |
0.722 |
|
| 2012 |
Modayil J, White A, Pilarski PM, Sutton RS. Acquiring a broad range of empirical knowledge in real time by temporal-difference learning Conference Proceedings - Ieee International Conference On Systems, Man and Cybernetics. 1903-1910. DOI: 10.1109/ICSMC.2012.6378016 |
0.386 |
|
| 2012 |
Silver D, Sutton RS, Müller M. Temporal-difference search in computer Go Machine Learning. 87: 183-219. DOI: 10.1007/S10994-012-5280-0 |
0.638 |
|
| 2012 |
Degris T, Pilarski PM, Sutton RS. Model-Free reinforcement learning with continuous action in practice Proceedings of the American Control Conference. 2177-2182. |
0.352 |
|
| 2011 |
Pilarski PM, Dawson MR, Degris T, Fahimi F, Carey JP, Sutton RS. Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning. Ieee ... International Conference On Rehabilitation Robotics : [Proceedings]. 2011: 5975338. PMID 22275543 DOI: 10.1109/ICORR.2011.5975338 |
0.33 |
|
| 2011 |
Sutton RS, Modayil J, Degris MDT, Pilarski PM, White A, Precup D. Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction 10th International Conference On Autonomous Agents and Multiagent Systems 2011, Aamas 2011. 2: 713-720. |
0.611 |
|
| 2010 |
Kehoe EJ, Ludvig EA, Sutton RS. Timing in trace conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus): scalar, nonscalar, and adaptive features. Learning & Memory (Cold Spring Harbor, N.Y.). 17: 600-4. PMID 21075900 DOI: 10.1101/Lm.1942210 |
0.64 |
|
| 2010 |
Maei HR, Szepesvari C, Bhatnagar S, Sutton RS. Toward off-policy learning control with function approximation Icml 2010 - Proceedings, 27th International Conference On Machine Learning. 719-726. |
0.784 |
|
| 2010 |
Maei HR, Sutton RS. GQ(λ): A general gradient algorithm for temporal-difference prediction learning with eligibility traces Artificial General Intelligence - Proceedings of the Third Conference On Artificial General Intelligence, Agi 2010. 91-96. |
0.784 |
|
| 2009 |
Kehoe EJ, Ludvig EA, Sutton RS. Magnitude and timing of conditioned responses in delay and trace classical conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus). Behavioral Neuroscience. 123: 1095-101. PMID 19824776 DOI: 10.1037/A0017112 |
0.632 |
|
| 2009 |
Kehoe EJ, Olsen KN, Ludvig EA, Sutton RS. Scalar timing varies with response magnitude in classical conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus). Behavioral Neuroscience. 123: 212-7. PMID 19170446 DOI: 10.1037/A0014122 |
0.646 |
|
| 2009 |
Sutton RS, Maei HR, Precup D, Bhatnagar S, Silver D, Szepesvári C, Wiewiora E. Fast gradient-descent methods for temporal-difference learning with linear function approximation Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 993-1000. DOI: 10.1145/1553374.1553501 |
0.792 |
|
| 2009 |
Bhatnagar S, Sutton RS, Ghavamzadeh M, Lee M. Natural actor-critic algorithms Automatica. 45: 2471-2482. DOI: 10.1016/j.automatica.2009.07.008 |
0.516 |
|
| 2009 |
Sutton RS, Szepesvári C, Maei HR. A convergent O(n) algorithm for off-policy temporal-difference learning with linear function approximation Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1609-1616. |
0.777 |
|
| 2009 |
Ludvig EA, Sutton RS, Verbeek E, Kehoe EJ. A computational model of hippocampal function in trace conditioning Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 993-1000. |
0.604 |
|
| 2009 |
Sutton RS, Maei HR, Precup D, Bhatnagar S, Silver D, Szepesvári C, Wiewiora E. Fast gradient-descent methods for temporal-difference learning with linear function approximation Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 993-1000. |
0.804 |
|
| 2009 |
Maei HR, Szepesvari C, Bhatnagar S, Precup D, Silver D, Sutton RS. Convergent temporal-difference learning with arbitrary smooth function approximation Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1204-1212. |
0.806 |
|
| 2008 |
Ludvig EA, Sutton RS, Kehoe EJ. Stimulus representation and the timing of reward-prediction errors in models of the dopamine system. Neural Computation. 20: 3034-54. PMID 18624657 DOI: 10.1162/Neco.2008.11-07-654 |
0.661 |
|
| 2008 |
Kehoe EJ, Ludvig EA, Dudeney JE, Neufeld J, Sutton RS. Magnitude and timing of nictitating membrane movements during classical conditioning of the rabbit (Oryctolagus cuniculus). Behavioral Neuroscience. 122: 471-6. PMID 18410186 DOI: 10.1037/0735-7044.122.2.471 |
0.638 |
|
| 2008 |
Cutumisu M, Szafron D, Bowling M, Sutton RS. Agent learning using action-dependent learning rates in computer role-playing games Proceedings of the 4th Artificial Intelligence and Interactive Digital Entertainment Conference, Aiide 2008. 22-29. |
0.362 |
|
| 2008 |
Silver D, Sutton RS, Müller M. Sample-based learning and search with permanent and transient memories Proceedings of the 25th International Conference On Machine Learning. 968-975. |
0.571 |
|
| 2007 |
Sutton RS, Koop A, Silver D. On the role of tracking in stationary environments Acm International Conference Proceeding Series. 227: 871-878. DOI: 10.1145/1273496.1273606 |
0.453 |
|
| 2007 |
Silver D, Sutton R, Müller M. Reinforcement learning of local shape in the game of go Ijcai International Joint Conference On Artificial Intelligence. 1053-1058. |
0.357 |
|
| 2006 |
Geramifard A, Bowling M, Sutton RS. Incremental least-squares temporal difference learning Proceedings of the National Conference On Artificial Intelligence. 1: 356-361. |
0.435 |
|
| 2005 |
Stone P, Sutton RS, Kuhlmann G. Reinforcement learning for RoboCup soccer keepaway Adaptive Behavior. 13: 165-188. DOI: 10.1177/105971230501300301 |
0.5 |
|
| 2005 |
Precup D, Sutton RS, Paduraru C, Koop A, Singh S. Off-policy learning with options and recognizers Advances in Neural Information Processing Systems. 1097-1104. |
0.618 |
|
| 2005 |
Rafols EJ, Ring MB, Sutton RS, Tanner B. Using predictive representations to improve generalization in reinforcement learning Ijcai International Joint Conference On Artificial Intelligence. 835-840. |
0.357 |
|
| 2002 |
Stone P, Sutton RS. Keepaway soccer: A machine learning testbed Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2377: 214-223. |
0.322 |
|
| 2001 |
Stone P, Sutton RS, Singh S. Reinforcement learning for 3 vs. 2 keepaway Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019: 249-258. |
0.395 |
|
| 2000 |
Sutton RS, McAllester D, Singh S, Mansour Y. Policy gradient methods for reinforcement learning with function approximation Advances in Neural Information Processing Systems. 1057-1063. |
0.389 |
|
| 1999 |
Sutton RS, Precup D, Singh S. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning Artificial Intelligence. 112: 181-211. DOI: 10.1016/S0004-3702(99)00052-1 |
0.675 |
|
| 1999 |
Moll R, Barto AG, Perkins TJ, Sutton RS. Learning instance-independent value functions to enhance local search Advances in Neural Information Processing Systems. 1017-1023. |
0.746 |
|
| 1999 |
Sutton RS. Open theoretical questions in reinforcement learning Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1572: 11-17. |
0.39 |
|
| 1999 |
Sutton RS, Singh S, Precup D, Ravindran B. Improved switching among temporally abstract actions Advances in Neural Information Processing Systems. 1066-1072. |
0.475 |
|
| 1999 |
Sutton RS. Reinforcement learning: Past, present and future? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1585: 195-197. |
0.394 |
|
| 1998 |
Sutton R, Barto A. Reinforcement Learning: An Introduction Ieee Transactions On Neural Networks. 9: 1054-1054. DOI: 10.1109/TNN.1998.712192 |
0.399 |
|
| 1998 |
Precupl D, Sutton RS, Satinder S. Theoretical results on reinforcement learning with temporally abstract options Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1398: 382-393. |
0.411 |
|
| 1998 |
Precup D, Sutton RS. Multi-time models for temporally abstract planning Advances in Neural Information Processing Systems. 1050-1056. |
0.494 |
|
| 1997 |
Santamaría JC, Ram A, Sutton RS. Experiments with reinforcement learning in problems with continuous state and action spaces Adaptive Behavior. 6: 163-217. DOI: 10.1177/105971239700600201 |
0.432 |
|
| 1997 |
Barto AG, Sutton RS. Chapter 19 Reinforcement learning in artificial intelligence Advances in Psychology. 121: 358-386. DOI: 10.1016/S0166-4115(97)80105-7 |
0.682 |
|
| 1996 |
Singh SP, Sutton RS. Reinforcement learning with replacing eligibility traces Machine Learning. 22: 123-158. DOI: 10.1007/Bf00114726 |
0.467 |
|
| 1992 |
Sutton RS, Barto AG, Williams RJ. Reinforcement Learning is Direct Adaptive Optimal Control Ieee Control Systems. 12: 19-22. DOI: 10.1109/37.126844 |
0.681 |
|
| 1992 |
Sutton RS. Introduction: The challenge of reinforcement learning Machine Learning. 8: 225-227. DOI: 10.1007/BF00992695 |
0.477 |
|
| 1991 |
Sutton RS. Dyna, an integrated architecture for learning, planning, and reacting Intelligence\/Sigart Bulletin. 2: 160-163. DOI: 10.1145/122344.122377 |
0.461 |
|
| 1991 |
Sutton RS. Planning by Incremental Dynamic Programming Machine Learning. 353-357. DOI: 10.1016/B978-1-55860-200-7.50073-8 |
0.305 |
|
| 1990 |
Whitehead SD, Sutton RS, Ballard DH. Advances in reinforcement learning and their implications for intelligent control . 1289-1297. |
0.371 |
|
| 1988 |
Sutton RS. Learning to Predict by the Methods of Temporal Differences Machine Learning. 3: 9-44. DOI: 10.1023/A:1022633531479 |
0.454 |
|
| 1988 |
Franklin JA, Sutton RS, Anderson CW. Application of connectionist learning methods to manufacturing process monitoring . 709-712. |
0.315 |
|
| 1986 |
Moore JW, Desmond JE, Berthier NE, Blazis DE, Sutton RS, Barto AG. Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: response topography, neuronal firing, and interstimulus intervals. Behavioural Brain Research. 21: 143-54. PMID 3755947 DOI: 10.1016/0166-4328(86)90092-6 |
0.592 |
|
| 1983 |
Barto AG, Sutton RS, Anderson CW. Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems Ieee Transactions On Systems, Man and Cybernetics. 834-846. DOI: 10.1109/TSMC.1983.6313077 |
0.616 |
|
| 1982 |
Barto AG, Anderson CW, Sutton RS. Synthesis of nonlinear control surfaces by a layered associative search network. Biological Cybernetics. 43: 175-85. PMID 7093360 DOI: 10.1007/BF00319977 |
0.612 |
|
| 1982 |
Barto AG, Sutton RS. Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element. Behavioural Brain Research. 4: 221-35. PMID 6277346 DOI: 10.1016/0166-4328(82)90001-8 |
0.577 |
|
| 1982 |
Barto AG, Sutton RS, Anderson CW. SPATIAL LEARNING SIMULATION SYSTEMS . 204-206. |
0.316 |
|
| 1981 |
Barto AG, Sutton RS. Landmark learning: an illustration of associative search. Biological Cybernetics. 42: 1-8. PMID 7326277 DOI: 10.1007/BF00335152 |
0.628 |
|
| 1981 |
Sutton RS, Barto AG. Toward a modern theory of adaptive networks: expectation and prediction. Psychological Review. 88: 135-70. PMID 7291377 DOI: 10.1037/0033-295X.88.2.135 |
0.624 |
|
| 1979 |
Barto AG, Sutton RS, Brouwer PS. Associative search network: A reinforcement learning associative memory Biological Cybernetics. 40: 201-211. DOI: 10.1007/BF00453370 |
0.587 |
|
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