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Average optimality for Markov decision processes in borel spaces: a new condition and approach

Part of: Stochastic systems and control

Published online by Cambridge University Press:  14 July 2016

Xianping Guo  and
Quanxin Zhu
Xianping Guo*
Affiliation:
Zhongshan University
Quanxin Zhu*
Affiliation:
South China Normal University
*
∗Postal address: School of Mathematics and Computational Science, Zhongshan University, Guangzhou, 510275, PR China. Email address: [email protected]
∗∗Postal address: Department of Mathematics, South China Normal University, Guangzhou, 510631, PR China. Email address: [email protected]
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Abstract

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In this paper we study discrete-time Markov decision processes with Borel state and action spaces. The criterion is to minimize average expected costs, and the costs may have neither upper nor lower bounds. We first provide two average optimality inequalities of opposing directions and give conditions for the existence of solutions to them. Then, using the two inequalities, we ensure the existence of an average optimal (deterministic) stationary policy under additional continuity-compactness assumptions. Our conditions are slightly weaker than those in the previous literature. Also, some new sufficient conditions for the existence of an average optimal stationary policy are imposed on the primitive data of the model. Moreover, our approach is slightly different from the well-known ‘optimality inequality approach’ widely used in Markov decision processes. Finally, we illustrate our results in two examples.

Keywords

Discrete-time Markov decision processaverage expected criterionaverage optimality inequalityoptimal stationary policy

MSC classification

Primary: 90C40: Markov and semi-Markov decision processes 93E20: Optimal stochastic control
Type
Research Papers
Information
Journal of Applied Probability , Volume 43 , Issue 2 , June 2006 , pp. 318 - 334
Copyright
© Applied Probability Trust 2006 

Footnotes

Partially supported by the NSFC, the NCET, and the RFDP.

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