Richard S. Sutton - Webdocs Cs Ualberta - University of Alberta

More documents

Recommendations

Info

$Formatting Instructions for Authors Using LaTeX - the Department of ...$

40. Ludvig, E., Sutton, R. S., Verbeek, E., Kehoe, E. J., “A computational model of hippocampal function in trace conditioning,” Advances in Neural Information Processing Systems 21, 8 pages, 2009. MIT Press. 41. Cutumisu, M., Szafron, D., Bowling, M., Sutton R. S., “Agent learning using actiondependent learning rates in computer role-playing games,” Proceedings of the 4th Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 22-29, 2008. 42. Sutton, R. S., Szepesvari, Cs., Geramifard, A., Bowling, M., “Dyna-style planning with linear function approximation and prioritized sweeping,” Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, pp. 528–536, 2008. 43. Silver, D., Sutton, R. S., Müller, M., “Sample-based learning and search with permanent and transient memories,” Proceedings of the 25th International Conference on Machine Learning, 2008. (27% Acceptance) 44. Bhatnagar, S., Sutton, R. S., Ghavamzadeh, M., Lee, M., “Incremental natural actor-critic algorithms,” Advances in Neural Information Processing Systems 20, 2008. 45. Sutton, R. S., Koop, A., Silver, D., “On the role of tracking in stationary environments,” Proceedings of the 24th International Conference on Machine Learning, 2007. 46. Silver, D., Sutton, R. S., Müller, M., “Reinforcement learning of local shape in the game of Go,” Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2007. 47. Geramifard, A., Bowling, M., Zinkevich, M., Sutton, R. S., “iLSTD: Eligibility traces and convergence analysis,” Advances in Neural Information Processing Systems 19, 2007. 48. Geramifard, A., Bowling, M., Sutton, R. S., “Incremental least-squares temporal difference learning,” Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 356-361, 2006. 49. Precup, D., Sutton, R. S., Paduraru, C., Koop, A., Singh, S., “Off-policy learning with options and recognizers,” Advances in Neural Information Processing Systems 18, 2006. 50. Sutton, R. S., Rafols, E. J., Koop, A., “Temporal abstraction in temporal-difference networks,” Advances in Neural Information Processing Systems 18, 2006. 51. Tanner, B., Sutton, R. S., “TD(λ) networks: Temporal-difference networks with eligibility traces,” Proceedings of the 22nd International Conference on Machine Learning (ICML), Bonn, Germany, 2005. 8 large typeset pages. 27% acceptance rate. 52. Rafols, E. J., Ring, M. B., Sutton, R. S., Tanner, B., “Using predictive representations to improve generalization in reinforcement learning,” Proceedings of the 19th International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, 2005. 6 large typeset pages. 18% acceptance rate. 12
53. Tanner, B., Sutton, R. S., “Temporal-difference networks with history,” Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland, 2005. 6 large typeset pages. 18% acceptance rate. 54. Sutton, R. S., Tanner, B., “Temporal-difference networks,” Advances in Neural Information Processing Systems 17. MIT Press, 2005. 55. Littman, M. L., Sutton, R. S., Singh, S., “Predictive representations of state,” Advances in Neural Information Processing Systems 14. MIT Press, 2002. 56. Stone, P., Sutton, R. S., “Scaling reinforcement learning toward RoboCup soccer,” Proceedings of the 18th International Conference on Machine Learning, pp. 537–544. Morgan Kaufmann, 2001. 57. Precup, D., Sutton, R. S., Dasgupta, S., “Off-policy temporal-difference learning with function approximation,” Proceedings of the 18th International Conference on Machine Learning, pp. 417–424. Morgan Kaufmann, 2001. 58. Sutton, R. S., McAllester, D., Singh, S., Mansour, Y., “Policy gradient methods for reinforcement learning with function approximation,” Advances in Neural Information Processing Systems 12 (Proceedings of the 1999 conference), pp. 1057–1063. MIT Press, 2000. 59. Precup, D., Sutton, R. S., Singh, S., “Eligibility traces for off-policy policy evaluation,” Proceedings of the 17th International Conference on Machine Learning, pp. 759–766. Morgan Kaufmann, 2000. 60. Sutton, R. S., Singh, S., Precup, D., Ravindran, B., “Improved switching among temporally abstract actions,” Advances in Neural Information Processing Systems 11 (Proceedings of the 1998 conference), pp. 1066–1072. MIT Press, 1999. 61. Moll, R., Barto, A. G., Perkins, T. J., Sutton, R. S., “Learning instance-independent value functions to enhance local search,” Advances in Neural Information Processing Systems 11 (Proceedings of the 1998 conference), pp. 1017–1023. MIT Press, 1999. 62. Sutton, R. S., Precup, D., Singh, S., “Intra-option learning about temporally abstract actions,” Proceedings of the 15th International Conference on Machine Learning, pp. 556–564. Morgan Kaufmann, 1998. 63. Precup, D., Sutton, R. S., Singh, S., “Theoretical results on reinforcement learning with temporally abstract options,” Machine Learning: ECML-98, Proceedings of the 10th European Conference on Machine Learning, pp. 382–393. Springer Verlag, 1998. 64. Precup, D., Sutton, R. S., “Multi-time models for temporally abstract planning,” Advances in Neural Information Processing Systems 10 (Proceedings of the 1997 conference), pp. 1050–1056. MIT Press, 1998. 13
Page 1 and 2: CURRICULUM VITAE Richard S. Sutton
Page 3 and 4: National Science Foundation to the
Page 5 and 6: Honors iCORE chair, 2003; renewed 2
Page 7 and 8: Selected Invited Presentations “L
Page 9 and 10: 5. Kehoe, E. J., Ludvig, E. A., Sut
Page 11: 30. Barto, A. G., Sutton, R. S., Wa
Page 15 and 16: 77. Sutton, R. S., “Integrated ar
Page 17 and 18: 99. Sutton, R. S., “Artificial in

Richard S. Sutton - Webdocs Cs Ualberta - University of Alberta

Create successful ePaper yourself

Delete template?

Save as template?