Markov decision processes with restricted observations: Finite horizon case

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1997-08-01

Author

Serin, Yaşar Yasemin
Avşar, Zeynep Müge

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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. (C) 1997 John Wiley & Sons, Inc.

Subject Keywords

Management Science and Operations Research, General Engineering, Modelling and Simulation, Ocean Engineering

URI

https://hdl.handle.net/11511/36042

Journal

NAVAL RESEARCH LOGISTICS

DOI

https://doi.org/10.1002/(sici)1520-6750(199708)44:5<439::aid-nav3>3.0.co;2-5

Collections

Department of Industrial Engineering, Article

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Citation Formats

Y. Y. Serin and Z. M. Avşar, “Markov decision processes with restricted observations: Finite horizon case,” NAVAL RESEARCH LOGISTICS, pp. 439–456, 1997, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36042.