Author(s)

Masayuki Kageyama, Takayuki Fujii, Koji Kanefuji, Hiroe Tsubaki

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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.

Markov Decision Processes, Conditional Value-at-Risk, Risk Optimal Policy, Inventory Model

Kageyama, M. , Fujii, T. , Kanefuji, K. and Tsubaki, H. (2011) Conditional Value-at-Risk for Random Immediate Reward Variables in Markov Decision Processes. American Journal of Computational Mathematics, 1, 183-188. doi: 10.4236/ajcm.2011.13021.