文章基本信息

标题：Conditional Value-at-Risk for Random Immediate Reward Variables in Markov Decision Processes
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作者：Masayuki Kageyama ; Takayuki Fujii ; Koji Kanefuji 等
期刊名称：American Journal of Computational Mathematics
印刷版ISSN：2161-1203
电子版ISSN：2161-1211
出版年度：2011
卷号：1
期号：3
页码：183-188
DOI：10.4236/ajcm.2011.13021
出版社：Scientific Research Publishing
摘要：We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.
关键词：Markov Decision Processes; Conditional Value-at-Risk; Risk Optimal Policy; Inventory Model