Papers on New Algorithms in RL

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  1. Learning Predictive State Representations by Satinder Singh, Michael Littman, Nicholas Jong, David Pardoe and Peter Stone. In Proceedings of the Twentieth International Conference on Machine Learning (ICML), pages 712-719, 2003.
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  2. Near-Optimal Reinforcement Learning in Polynomial Time by Michael Kearns and Satinder Singh. In Machine Learning journal, Volume 49, Issue 2, pages 209-232, 2002.
    ( shorter version appears in ICML 1998).
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  3. Eligibility Traces for Off-Policy Policy Evaluation by Doina Precup, Richard Sutton, and Satinder Singh. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 759-766, 2000.
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  4. Policy Gradient Methods for Reinforcement Learning with Function Approximation by Richard Sutton, Dave McAllester, Satinder Singh and Yishay Mansour. In Advances in Neural Information Processing Systems 12 (NIPS), 2000.
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  5. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning by Richard Sutton, Doina Precup and Satinder Singh. In Artificial Intelligence Journal, Volume 112, pages 181-211, 1999.
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  6. Approximate Planning for Factored POMDPs using Belief State Simplification by Dave McAllester and Satinder Singh. In Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), pages 409-416, 1999.
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  7. Improved switching among temporally abstract actions by Richard Sutton, Satinder Singh, Doina Precup and Balaraman Ravindran. In Advances in Neural Information Processing Systems 11 (NIPS), pages 1066-1072, 1999.
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  8. Near-Optimal Reinforcement Learning in Polynomial Time by Michael Kearns and Satinder Singh. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML), pages 260-268, 1998.
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  9. Intra-Option Learning about Temporally Abstract Actions by Richard Sutton, Doina Precup and Satinder Singh. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML), pages 556-564, 1998.
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  10. How to Dynamically Merge Markov Decision Processes by Satinder Singh and David Cohn. In Advances in Neural Information Processing Systems 10 (NIPS), pages 1057-1063, 1998.
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  11. Reinforcement Learning with Replacing Eligibility Traces by Satinder Singh and Richard Sutton. In Machine Learning journal, Volume 22, Issue 1, pages 123-158, 1996.
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  12. Long Term Potentiation, Navigation and Dynamic Programming by Peter Dayan and Satinder Singh. In Proceedings of Computation and Neural Systems Meeting (CNS) 1996.
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  13. Improving Policies Without Measuring Merits by Peter Dayan and Satinder Singh. In Advances in Neural Information Processing Systems 8 (NIPS), pages 1059-1065, 1996.
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  14. Learning to Act using Real-Time Dynamic Programming by Andrew Barto, Steve Bradtke and Satinder Singh. In Artificial Intelligence, Volume 72, pages 81-138, 1995.
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  15. Reinforcement Learning With Soft State Aggregation by Satinder Singh, Tommi Jaakkola and Michael Jordan. In Advances in Neural Information Processing Systems 7 (NIPS), pages 361-368, 1995.
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  16. Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes by Satinder Singh. In Proceedings of the Twelth National Conference on Artificial Intelligence (AAAI), pages 700-705, 1994.
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  17. Learning Without State-Estimation in Partially Observable Markovian Decision Processes by Satinder Singh, Tommi Jaakkola and Michael Jordan. In Machine Learning: Proceedings of the Eleventh International Conference (ICML), pages 284-292, 1994.
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  18. Robust Reinforcement Learning in Motion Planning by Satinder Singh, Andrew Barto, Roderic Grupen, and Christopher Connolly. In Advances in Neural Information Processing Systems 6 (NIPS), pages 655-662, 1994.
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  19. Reinforcement Learning with a Hierarchy of Abstract Models by Satinder Singh. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI), pages 202-207, 1992.
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  20. Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models by Satinder Singh. In Proceedings of the Ninth Machine Learning Conference, pages 406-415, 1992.
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  21. Transfer of Learning by Composing Solutions of Elemental Sequential Tasks by Satinder Singh. In Machine Learning Journal, Volume 8, Issue 3, pages 323-339, 1992.
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  22. The Efficient Learning of Multiple Task Sequences by Satinder Singh. In Advances in Neural Information Processing Systems 4 (NIPS), pages 251-258, 1992.
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  23. Transfer of Learning Across Compositions of Sequential Tasks by Satinder Singh. In Machine Learning: Proceedings of the Eighth International Workshop, pages 348-352, 1991.
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