Abstract
In this note we consider a non-stationary stochastic decision model with vector-valued reward. Based on Pareto-optimality we define the maximal total reward as a set of vector valued total rewards, which have not a successor with respect to the underlying partially order relation. The principle of optimality is derived. Using the well-known von-Neumann-Morgenstern-property we formulate a Bellman-equation, which consists in a system of iterative set relations.
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