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Risk-Sensitive Markov Decision Processes
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DOI：10.1287/mnsc.18.7.356.png)
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Abstract
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This paper considers the maximization of certain equivalent reward generated by a Markov decision process with constant risk sensitivity. First, value iteration is used to optimize possibly time-varying processes of finite duration. Then a policy iteration procedure is developed to find the stationary policy with highest certain equivalent gain for the infinite duration case. A simple example demonstrates both procedures.
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