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Markov decision processes with restricted observations: Finite horizon case

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  • Yasemin Serin
  • Zeynep Muge Avsar

Abstract

In this article we consider a Markov decision process subject to the constraints that result from some observability restrictions. We assume that the state of the Markov process under consideration is unobservable. The states are grouped so that the group that a state belongs to is observable. So, we want to find an optimal decision rule depending on the observable groups instead of the states. This means that the same decision applies to all the states in the same group. We prove that a deterministic optimal policy exists for the finite horizon. An algorithm is developed to compute policies minimizing the total expected discounted cost over a finite horizon. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 439–456, 1997

Suggested Citation

  • Yasemin Serin & Zeynep Muge Avsar, 1997. "Markov decision processes with restricted observations: Finite horizon case," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(5), pages 439-456, August.
    • Handle: RePEc:wly:navres:v:44:y:1997:i:5:p:439-456
    DOI: 10.1002/(SICI)1520-6750(199708)44:53.0.CO;2-5
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    References listed on IDEAS

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    1. Keith W. Ross & Ravi Varadarajan, 1989. "Markov Decision Processes with Sample Path Constraints: The Communicating Case," Operations Research, INFORMS, vol. 37(5), pages 780-790, October.
    2. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
    3. Chelsea C. White & William T. Scherer, 1994. "Finite-Memory Suboptimal Design for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 42(3), pages 439-455, June.
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    2. Stadje, Wolfgang, 2005. "The evolution of aggregated Markov chains," Statistics & Probability Letters, Elsevier, vol. 74(4), pages 303-311, October.

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