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Conditional Value-at-Risk for Random Immediate Reward Variables in Markov Decision Processes

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Author(s): Masayuki Kageyama | Takayuki Fujii | Koji Kanefuji | Hiroe Tsubaki

Journal: American Journal of Computational Mathematics
ISSN 2161-1203

Volume: 01;
Issue: 03;
Start page: 183;
Date: 2011;
Original page

Keywords: Markov Decision Processes | Conditional Value-at-Risk | Risk Optimal Policy | Inventory Model

ABSTRACT
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.
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