Implementable policies: Discounted cost case

Date

1995-01-18

Author

Serin, Yaşar Yasemin

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We consider a Markov decision process (MDP) with finite state space S and finite action set A. The state space is partitioned into K sets S-1, S-2, ..., S-K. A stationary randomized policy is described by the parameters {alpha(ia), i is an element of S, a is an element of A}, where alpha(ia) = the parobability that action a is taken when the system is in state i. A policy is called implementable if alpha(ia) = alpha(ja) for all a is an element of A whenever states i and j belong to a common subset S-r for some r = 1, 2,..., K. In this paper we discuss the importance of implementable policies and present an algorithm to find implementable policies that (locally) minimize the expected total discounted cost over infinite horizon.

Subject Keywords

Optimal policy, Directional derivative, Markov decision process, Descent direction, Retrial queue

URI

https://hdl.handle.net/11511/52735

Collections

Department of Industrial Engineering, Conference / Seminar

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Citation Formats

Y. Y. Serin, “Implementable policies: Discounted cost case,” 1995, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52735.