Richard S. Sutton

	2009
50		Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora: Fast gradient-descent methods for temporal-difference learning with linear function approximation. ICML 2009: 125
	2008
49		Maria Cutumisu, Duane Szafron, Michael H. Bowling, Richard S. Sutton: Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games. AIIDE 2008
48		David Silver, Richard S. Sutton, Martin Müller: Sample-based learning and search with permanent and transient memories. ICML 2008: 968-975
47		Richard S. Sutton, Csaba Szepesvári, Hamid Reza Maei: A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation. NIPS 2008: 1609-1616
46		Elliot A. Ludvig, Richard S. Sutton, Eric Verbeek, E. James Kehoe: A computational model of hippocampal function in trace conditioning. NIPS 2008: 993-1000
45		Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael H. Bowling: Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping. UAI 2008: 528-536
44		Elliot A. Ludvig, Richard S. Sutton, E. James Kehoe: Stimulus Representation and the Timing of Reward-Prediction Errors in Models of the Dopamine System. Neural Computation 20(12): 3034-3054 (2008)
	2007
43		Richard S. Sutton, Anna Koop, David Silver: On the role of tracking in stationary environments. ICML 2007: 871-878
42		David Silver, Richard S. Sutton, Martin Müller: Reinforcement Learning of Local Shape in the Game of Go. IJCAI 2007: 1053-1058
41		Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee: Incremental Natural Actor-Critic Algorithms. NIPS 2007
	2006
40		Alborz Geramifard, Michael H. Bowling, Richard S. Sutton: Incremental Least-Squares Temporal Difference Learning. AAAI 2006
39		Alborz Geramifard, Michael H. Bowling, Martin Zinkevich, Richard S. Sutton: iLSTD: Eligibility Traces and Convergence Analysis. NIPS 2006: 441-448
	2005
38		Brian Tanner, Richard S. Sutton: TD(lambda) networks: temporal-difference networks with eligibility traces. ICML 2005: 888-895
37		Eddie J. Rafols, Mark B. Ring, Richard S. Sutton, Brian Tanner: Using Predictive Representations to Improve Generalization in Reinforcement Learning. IJCAI 2005: 835-840
36		Brian Tanner, Richard S. Sutton: Temporal-Difference Networks with History. IJCAI 2005: 865-870
35		Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder P. Singh: Off-policy Learning with Options and Recognizers. NIPS 2005
34		Richard S. Sutton, Eddie J. Rafols, Anna Koop: Temporal Abstraction in Temporal-difference Networks. NIPS 2005
	2004
33		Richard S. Sutton, Brian Tanner: Temporal-Difference Networks. NIPS 2004
	2001
32		Doina Precup, Richard S. Sutton, Sanjoy Dasgupta: Off-Policy Temporal Difference Learning with Function Approximation. ICML 2001: 417-424
31		Peter Stone, Richard S. Sutton: Scaling Reinforcement Learning toward RoboCup Soccer. ICML 2001: 537-544
30		Michael L. Littman, Richard S. Sutton, Satinder P. Singh: Predictive Representations of State. NIPS 2001: 1555-1561
29		Peter Stone, Richard S. Sutton: Keepaway Soccer: A Machine Learning Testbed. RoboCup 2001: 214-223
	2000
28		Doina Precup, Richard S. Sutton, Satinder P. Singh: Eligibility Traces for Off-Policy Policy Evaluation. ICML 2000: 759-766
27		Peter Stone, Richard S. Sutton, Satinder P. Singh: Reinforcement Learning for 3 vs. 2 Keepaway RoboCup 2000: 249-258
	1999
26		Richard S. Sutton: Open Theoretical Questions in Reinforcement Learning. EuroCOLT 1999: 11-17
25		Richard S. Sutton, David A. McAllester, Satinder P. Singh, Yishay Mansour: Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS 1999: 1057-1063
24		Richard S. Sutton, Doina Precup, Satinder P. Singh: Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artif. Intell. 112(1-2): 181-211 (1999)
	1998
23		Doina Precup, Richard S. Sutton, Satinder P. Singh: Theoretical Results on Reinforcement Learning with Temporally Abstract Options. ECML 1998: 382-393
22		Richard S. Sutton, Doina Precup, Satinder P. Singh: Intra-Option Learning about Temporally Abstract Actions. ICML 1998: 556-564
21		Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton: Learning Instance-Independent Value Functions to Enhance Local Search. NIPS 1998: 1017-1023
20		Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran: Improved Switching among Temporally Abstract Actions. NIPS 1998: 1066-1072
19		Richard S. Sutton: Reinforcement Learning: Past, Present and Future. SEAL 1998: 195-197
18		Richard S. Sutton, Andrew G. Barto: Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks 9(5): 1054-1054 (1998)
	1997
17		Richard S. Sutton: On the Significance of Markov Decision Processes. ICANN 1997: 273-282
16		Doina Precup, Richard S. Sutton: Exponentiated Gradient Methods for Reinforcement Learning. ICML 1997: 272-277
15		Doina Precup, Richard S. Sutton: Multi-time Models for Temporally Abstract Planning. NIPS 1997
	1996
14		Satinder P. Singh, Richard S. Sutton: Reinforcement Learning with Replacing Eligibility Traces. Machine Learning 22(1-3): 123-158 (1996)
	1995
13		Richard S. Sutton: TD Models: Modeling the World at a Mixture of Time Scales. ICML 1995: 531-539
12		Richard S. Sutton: Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. NIPS 1995: 1038-1044
	1993
11		Richard S. Sutton, Steven D. Whitehead: Online Learning with Random Representations. ICML 1993: 314-321
	1992
10		Richard S. Sutton: Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta. AAAI 1992: 171-176
	1991
9		Richard S. Sutton, Christopher J. Matheus: Learning Polynomial Functions by Feature Construction. ML 1991: 208-212
8		Richard S. Sutton: Planning by Incremental Dynamic Programming. ML 1991: 353-357
7		Terence D. Sanger, Richard S. Sutton, Christopher J. Matheus: Iterative Construction of Sparse Polynomial Approximations. NIPS 1991: 1064-1071
6		Richard S. Sutton: Dyna, an Integrated Architecture for Learning, Planning, and Reacting. SIGART Bulletin 2(4): 160-163 (1991)
	1990
5		Richard S. Sutton: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. ML 1990: 216-224
4		Richard S. Sutton: Integrated Modeling and Control Based on Reinforcement Learning. NIPS 1990: 471-478
	1989
3		Andrew G. Barto, Richard S. Sutton, Christopher J. C. H. Watkins: Sequential Decision Probelms and Neural Networks. NIPS 1989: 686-693
	1988
2		Richard S. Sutton: Learning to Predict by the Methods of Temporal Differences. Machine Learning 3: 9-44 (1988)
	1985
1		Oliver G. Selfridge, Richard S. Sutton, Andrew G. Barto: Training and Tracking in Robotics. IJCAI 1985: 670-672

Coauthor Index

1	Andrew G. Barto	[1] [3] [18] [21]
2	Shalabh Bhatnagar	[41] [50]
3	Michael H. Bowling	[39] [40] [45] [49]
4	Maria Cutumisu	[49]
5	Sanjoy Dasgupta	[32]
6	Alborz Geramifard	[39] [40] [45]
7	Mohammad Ghavamzadeh	[41]
8	E. James Kehoe	[44] [46]
9	Anna Koop	[34] [35] [43]
10	Mark Lee	[41]
11	Michael L. Littman	[30]
12	Elliot A. Ludvig	[44] [46]
13	Hamid Reza Maei	[47] [50]
14	Yishay Mansour	[25]
15	Christopher J. Matheus	[7] [9]
16	David A. McAllester	[25]
17	Robert Moll (Robert N. Moll)	[21]
18	Martin Müller	[42] [48]
19	Cosmin Paduraru	[35]
20	Theodore J. Perkins	[21]
21	Doina Precup	[15] [16] [20] [22] [23] [24] [28] [32] [35] [50]
22	Eddie J. Rafols	[34] [37]
23	Balaraman Ravindran	[20]
24	Mark B. Ring	[37]
25	Terence D. Sanger	[7]
26	Oliver G. Selfridge	[1]
27	David Silver	[42] [43] [48] [50]
28	Satinder P. Singh	[14] [20] [22] [23] [24] [25] [27] [28] [30] [35]
29	Peter Stone	[27] [29] [31]
30	Duane Szafron	[49]
31	Csaba Szepesvári	[45] [47] [50]
32	Brian Tanner	[33] [36] [37] [38]
33	H. M. W. (Eric) Verbeek (H. M. W. Verbeek, Eric Verbeek)	[46]
34	Christopher J. C. H. Watkins	[3]
35	Steven D. Whitehead	[11]
36	Eric Wiewiora	[50]
37	Martin Zinkevich	[39]

Colors in the list of coauthors