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Implementable Policies: Discounted Cost Case

In: Computations with Markov Chains

Author

Listed:
  • Yasemin Serin

    (Middle East Technical University)

  • Vidyadhar G. Kulkarni

    (The University of North Carolina at Chapel Hill)

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.

Suggested Citation

  • Yasemin Serin & Vidyadhar G. Kulkarni, 1995. "Implementable Policies: Discounted Cost Case," Springer Books, in: William J. Stewart (ed.), Computations with Markov Chains, chapter 17, pages 283-306, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-2241-6_17
    DOI: 10.1007/978-1-4615-2241-6_17
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