Sample-path optimality and variance-maximization for Markov decision processes

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• 作者：Q. X. Zhu
• 关键词：Discrete ; time Markov decision process ; Unbounded reward ; Sample ; path reward criterion ; Variance ; maximization ; Optimal stationary policy
• 刊名：Mathematical Methods of Operations Research (ZOR)
• 出版年：2007
• 出版时间：June, 2007
• 年：2007
• 卷：65
• 期：3
• 页码：519-538
• 全文大小：176 KB

文摘

This paper studies both the average sample-path reward (ASPR) criterion and the limiting average variance criterion for denumerable discrete-time Markov decision processes. The rewards may have neither upper nor lower bounds. We give sufficient conditions on the system’s primitive data and under which we prove the existence of ASPR-optimal stationary policies and variance optimal policies. Our conditions are weaker than those in the previous literature. Moreover, our results are illustrated by a controlled queueing system.