Abstract
In this paper we consider finite-stage stochastic optimization problems of utility criterion, which is the stochastic evaluation of associative reward through a utility function. We optimize the expected value of a utility criterion not in the class of Markov policies but in the class of general policies. We show that, by expanding the state space, an invariant imbedding approach yields an recursive relation between two adjacent optimal value functions. We show that the utility problem with a general policy is equivalent to a terminal problem with a Markov policy on the augmented state space. Finally it is shown that the utility problem has an optimal policy in the class of general policies on the original state space.
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