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On Approximate Solutions for Partially Observable Decision Problems

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  • Juri Hinz

    (University of Technology Sydney)

Abstract

Decision problems under partial observation serve a rich framework for planning and control of operations in diverse applications. In this area, approximate point-based algorithms have emerged improving the scalability of existing numerical schemes to a key level where they can be broadly used in robotics. We elaborate on some core principles of point-based methods and present an approximate solution technique which utilizes linear state dynamics and convexity.

Suggested Citation

  • Juri Hinz, 2021. "On Approximate Solutions for Partially Observable Decision Problems," Research Paper Series 421, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:421
    as

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    File URL: https://www.uts.edu.au/sites/default/files/article/downloads/rp421.pdf
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    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. William S. Lovejoy, 1991. "Computationally Feasible Bounds for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 39(1), pages 162-175, February.
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