Markov decision processes under observability constraints

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....

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Bibliographic Details
Published in:Mathematical methods of operations research (Heidelberg, Germany) Germany), 2005-06, Vol.61 (2), p.311-328
Main Authors: Serin, Yasemin, Kulkarni, Vidyadhar
Format: Article
Language:English
Subjects:
Algorithms
Decision theory
Linear programming
Markov analysis
Operations research
Random variables
Studies
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Summary: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. [PUBLICATION ABSTRACT]
ISSN:1432-2994
1432-5217
DOI:10.1007/s001860400402