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Markov decision processes under observability constraints

Author

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  • Yasemin Serin
  • Vidyadhar Kulkarni

Abstract

We develop an algorithm to compute optimal policies for Markov decision processes subject to constraints that result from some observability restrictions on the process. We assume that the state of the Markov process is unobservable. There is an observable process related to the unobservable state. So, we want to find a decision rule depending only on this observable process. The objective is to minimize the expected average cost over an infinite horizon. We also analyze the possibility of performing observations in more detail to obtain improved policies. Copyright Springer-Verlag 2005

Suggested Citation

  • Yasemin Serin & Vidyadhar Kulkarni, 2005. "Markov decision processes under observability constraints," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 61(2), pages 311-328, June.
    • Handle: RePEc:spr:mathme:v:61:y:2005:i:2:p:311-328
    DOI: 10.1007/s001860400402
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    Cited by:

    1. Lauren B. Davis & Thom J. Hodgson & Russell E. King & Wenbin Wei, 2009. "Technical note: A computationally efficient algorithm for undiscounted Markov decision processes with restricted observations," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(1), pages 86-92, February.
    2. Lanlan Zhang & Xianping Guo, 2008. "Constrained continuous-time Markov decision processes with average criteria," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 67(2), pages 323-340, April.

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