Implementable Policies: Discounted Cost Case

Yasemin Serin and Vidyadhar G. Kulkarni
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Yasemin Serin: Middle East Technical University
Vidyadhar G. Kulkarni: The University of North Carolina at Chapel Hill

Chapter 17 in Computations with Markov Chains, 1995, pp 283-306 from Springer

Abstract: Abstract 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 {α ia i ∈, S, a ∈ A}, where αia = the parobability that action a is taken when the system is in state i. A policy is called implementable if α ia = α ja for all α ∈ A whenever states i and j belong to a common subset S τ 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.

Keywords: Optimal Policy; Directional Derivative; Markov Decision Process; Descent Direction; Retrial Queue (search for similar items in EconPapers)
Date: 1995
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-2241-6_17

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DOI: 10.1007/978-1-4615-2241-6_17

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