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Semi-infinite Markov decision processes

Author

Listed:
  • Ming Chen
  • Jerzy A. Filar
  • Ke Liu

Abstract

In this paper discounted and average Markov decision processes with finite state space and countable action set (semi-infinite MDP for short) are discussed. Without ordinary continuity and compactness conditions, for discounted semi-infinite MDP we have shown that by exploiting the results on semi-infinite linear programming due to Tijs [20] our semi-infinite discounted MDP can be approximated by a sequence of finite discounted MDPs and even in a semi-infinite discounted MDP it is sufficient to restrict ourselves to the class of deterministic stationary strategies. For average reward case we still prove that under some conditions the supremum in the class of general strategies is equivalent to the supremum in the class of deterministic stationary strategies. A counterexample shows that these conditions can not be easily relaxed. Copyright Springer-Verlag Berlin Heidelberg 2000

Suggested Citation

  • Ming Chen & Jerzy A. Filar & Ke Liu, 2000. "Semi-infinite Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 51(1), pages 115-137, February.
  • Handle: RePEc:spr:mathme:v:51:y:2000:i:1:p:115-137
    DOI: 10.1007/s001860050006
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