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Finding Best k Policies

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Algorithmic Decision Theory (ADT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5783))

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Abstract

An optimal probabilistic-planning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k best policies problem. The problem is to find the k best policies. The k best policies, k > 1, cannot be found directly using dynamic programming. Naïvely, finding the k-th best policy can be Turing reduced to the optimal planning problem, but the number of problems queried in the naïve algorithm is exponential in k. We show empirically that solving k best policy problem by using this reduction requires unreasonable amounts of time even when k = 3. We then provide a new algorithm, based on our theoretical contribution to prove that the k-th best policy differs from the i-th policy, for some i < k, on exactly one state. We show that the time complexity of the algorithm is quadratic in k, but the number of optimal planning problems it solves is linear in k. We demonstrate empirically that the new algorithm has good scalability.

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Author information

Authors and Affiliations

  1. Computer Science & Engineering University of Washington, Seattle, WA, US, 98195-2350

    Peng Dai

  2. Dept. of Comp. Sci. Lexington, Univ. of Kentucky, KY, USA, 40506-0046

    Judy Goldsmith

Authors
  1. Peng Dai
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  2. Judy Goldsmith
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Editor information

Editors and Affiliations

  1. Department of Pure and Applied Mathematics, University of Padova, Via Trieste 63, 35121, Padova, Italy

    Francesca Rossi

  2. LAMSADE-CNRS, University of Paris Dauphine, Place du Maréchal De Lattre de Tassigny, 75775, Paris Cedex 16, France

    Alexis Tsoukias

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© 2009 Springer-Verlag Berlin Heidelberg

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Dai, P., Goldsmith, J. (2009). Finding Best k Policies. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_13

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  • DOI: https://doi.org/10.1007/978-3-642-04428-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04427-4

  • Online ISBN: 978-3-642-04428-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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