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Richard S. Sutton: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Richard S. Sutton
    Learning to Predict by the Methods of Temporal Differences. [Citation Graph (1, 0)][DBLP]
    Machine Learning, 1988, v:3, n:, pp:9-44 [Journal]
  2. Alborz Geramifard, Michael Bowling, Richard S. Sutton
    Incremental Least-Squares Temporal Difference Learning. [Citation Graph (0, 0)][DBLP]
    AAAI, 2006, pp:- [Conf]
  3. Richard S. Sutton
    Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta. [Citation Graph (0, 0)][DBLP]
    AAAI, 1992, pp:171-176 [Conf]
  4. Doina Precup, Richard S. Sutton, Satinder P. Singh
    Theoretical Results on Reinforcement Learning with Temporally Abstract Options. [Citation Graph (0, 0)][DBLP]
    ECML, 1998, pp:382-393 [Conf]
  5. Richard S. Sutton
    Open Theoretical Questions in Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1999, pp:11-17 [Conf]
  6. Richard S. Sutton
    On the Significance of Markov Decision Processes. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:273-282 [Conf]
  7. Doina Precup, Richard S. Sutton
    Exponentiated Gradient Methods for Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:272-277 [Conf]
  8. Doina Precup, Richard S. Sutton, Sanjoy Dasgupta
    Off-Policy Temporal Difference Learning with Function Approximation. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:417-424 [Conf]
  9. Doina Precup, Richard S. Sutton, Satinder P. Singh
    Eligibility Traces for Off-Policy Policy Evaluation. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:759-766 [Conf]
  10. Peter Stone, Richard S. Sutton
    Scaling Reinforcement Learning toward RoboCup Soccer. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:537-544 [Conf]
  11. Richard S. Sutton
    Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. [Citation Graph (0, 0)][DBLP]
    ML, 1990, pp:216-224 [Conf]
  12. Richard S. Sutton
    Planning by Incremental Dynamic Programming. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:353-357 [Conf]
  13. Richard S. Sutton
    TD Models: Modeling the World at a Mixture of Time Scales. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:531-539 [Conf]
  14. Richard S. Sutton, Christopher J. Matheus
    Learning Polynomial Functions by Feature Construction. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:208-212 [Conf]
  15. Richard S. Sutton, Doina Precup, Satinder P. Singh
    Intra-Option Learning about Temporally Abstract Actions. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:556-564 [Conf]
  16. Richard S. Sutton, Steven D. Whitehead
    Online Learning with Random Representations. [Citation Graph (0, 0)][DBLP]
    ICML, 1993, pp:314-321 [Conf]
  17. Brian Tanner, Richard S. Sutton
    TD(lambda) networks: temporal-difference networks with eligibility traces. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:888-895 [Conf]
  18. Eddie J. Rafols, Mark B. Ring, Richard S. Sutton, Brian Tanner
    Using Predictive Representations to Improve Generalization in Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:835-840 [Conf]
  19. Oliver G. Selfridge, Richard S. Sutton, Andrew G. Barto
    Training and Tracking in Robotics. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1985, pp:670-672 [Conf]
  20. Brian Tanner, Richard S. Sutton
    Temporal-Difference Networks with History. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:865-870 [Conf]
  21. David Silver, Richard S. Sutton, Martin Müller 0003
    Reinforcement Learning of Local Shape in the Game of Go. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:1053-1058 [Conf]
  22. Andrew G. Barto, Richard S. Sutton, Christopher J. C. H. Watkins
    Sequential Decision Probelms and Neural Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1989, pp:686-693 [Conf]
  23. Michael L. Littman, Richard S. Sutton, Satinder P. Singh
    Predictive Representations of State. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1555-1561 [Conf]
  24. Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton
    Learning Instance-Independent Value Functions to Enhance Local Search. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:1017-1023 [Conf]
  25. Doina Precup, Richard S. Sutton
    Multi-time Models for Temporally Abstract Planning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  26. Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder P. Singh
    Off-policy Learning with Options and Recognizers. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  27. Terence D. Sanger, Richard S. Sutton, Christopher J. Matheus
    Iterative Construction of Sparse Polynomial Approximations. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:1064-1071 [Conf]
  28. Richard S. Sutton
    Integrated Modeling and Control Based on Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1990, pp:471-478 [Conf]
  29. Richard S. Sutton
    Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:1038-1044 [Conf]
  30. Richard S. Sutton, David A. McAllester, Satinder P. Singh, Yishay Mansour
    Policy Gradient Methods for Reinforcement Learning with Function Approximation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1057-1063 [Conf]
  31. Richard S. Sutton, Eddie J. Rafols, Anna Koop
    Temporal Abstraction in Temporal-difference Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  32. Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran
    Improved Switching among Temporally Abstract Actions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:1066-1072 [Conf]
  33. Richard S. Sutton, Brian Tanner
    Temporal-Difference Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  34. Peter Stone, Richard S. Sutton
    Keepaway Soccer: A Machine Learning Testbed. [Citation Graph (0, 0)][DBLP]
    RoboCup, 2001, pp:214-223 [Conf]
  35. Peter Stone, Richard S. Sutton, Satinder P. Singh
    Reinforcement Learning for 3 vs. 2 Keepaway [Citation Graph (0, 0)][DBLP]
    RoboCup, 2000, pp:249-258 [Conf]
  36. Richard S. Sutton
    Reinforcement Learning: Past, Present and Future. [Citation Graph (0, 0)][DBLP]
    SEAL, 1998, pp:195-197 [Conf]
  37. Richard S. Sutton, Doina Precup, Satinder P. Singh
    Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1999, v:112, n:1-2, pp:181-211 [Journal]
  38. Satinder P. Singh, Richard S. Sutton
    Reinforcement Learning with Replacing Eligibility Traces. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:22, n:1-3, pp:123-158 [Journal]
  39. Richard S. Sutton
    Dyna, an Integrated Architecture for Learning, Planning, and Reacting. [Citation Graph (0, 0)][DBLP]
    SIGART Bulletin, 1991, v:2, n:4, pp:160-163 [Journal]
  40. Richard S. Sutton, Anna Koop, David Silver
    On the role of tracking in stationary environments. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:871-878 [Conf]
  41. Alborz Geramifard, Michael Bowling, Martin Zinkevich, Richard S. Sutton
    iLSTD: Eligibility Traces and Convergence Analysis. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:441-448 [Conf]

  42. Sample-based learning and search with permanent and transient memories. [Citation Graph (, )][DBLP]


  43. Fast gradient-descent methods for temporal-difference learning with linear function approximation. [Citation Graph (, )][DBLP]


  44. Toward Off-Policy Learning Control with Function Approximation. [Citation Graph (, )][DBLP]


  45. Incremental Natural Actor-Critic Algorithms. [Citation Graph (, )][DBLP]


  46. A computational model of hippocampal function in trace conditioning. [Citation Graph (, )][DBLP]


  47. A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation. [Citation Graph (, )][DBLP]


  48. Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping. [Citation Graph (, )][DBLP]


  49. Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games. [Citation Graph (, )][DBLP]


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