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Implementable policies: Discounted cost case
Date
1995-01-18
Author
Serin, Yaşar Yasemin
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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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Y. Y. Serin, “Implementable policies: Discounted cost case,” 1995, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52735.