Papers on dealing with Hidden State in RL

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  1. Predictive State Representations: A New Theory for Modeling Dynamical Systems by Satinder Singh, Michael R. James and Matthew R. Rudary. In Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (UAI), pages 512-519, 2004.
    pdf.

  2. Learning and Discovery of Predictive State Representations in Dynamical Systems with Reset by Michael James and Satinder Singh. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML), pages 417-424, 2004.
    pdf.

  3. A Nonlinear Predictive State Representation by Matthew Rudary and Satinder Singh. In Advances in Neural Information Processing Systems 16 (NIPS), pages 855-862, 2004.
    pdf.

  4. 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.
    gzipped postscript.

  5. Predictive Representations of State by Michael Littman, Richard Sutton and Satinder Singh. In Advances in Neural Information Processing Systems 14 (NIPS), pages 1555-1561, 2002.
    gzipped postscript pdf.

  6. Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes by John K. Williams and Satinder Singh. In Advances in Neural Information Processing Systems 11 (NIPS), pages 1073-1079, 1999.
    gzipped postscript.

  7. Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes by John Loch and Satinder Singh. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML), pages 323-331, 1998.
    gzipped postscript.
Click here to return to the main page on reinforcement learning Satinder Singh.