A NONLINEAR-PROGRAMMING MODEL FOR PARTIALLY OBSERVABLE MARKOV DECISION-PROCESSES - FINITE-HORIZON CASE

Date

1995-11-02

Author

Serin, Yaşar Yasemin

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The concept of partially observable Markov decision processes was born to handle the problem of lack of information about the state of a Markov decision process. If the state of the system is unknown to the decision maker then an obvious approach is to gather information that is helpful in selecting an action, This problem was already solved using the theory of Markov processes. We construct a nonlinear programming model for the same problem and develop a solution algorithm that turns out to be a policy iteration algorithm. The policies found this way are easier to use than the policies found by the existing method, although they have the same optimal objective value.

Subject Keywords

Partially observable markov decision process, Method of feasible directions

URI

https://hdl.handle.net/11511/47690

Journal

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

DOI

https://doi.org/10.1016/0377-2217(94)00091-p

Collections

Department of Industrial Engineering, Article

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Citation Formats

Y. Y. Serin, “A NONLINEAR-PROGRAMMING MODEL FOR PARTIALLY OBSERVABLE MARKOV DECISION-PROCESSES - FINITE-HORIZON CASE,” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, pp. 549–564, 1995, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47690.