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Constrained Markov decision processes with compact state and action spaces are studied under long-run average reward or cost criteria. And introducing a corresponding Lagrange function, a saddle-point theorem is given, by which the existence of a constrained optimal pair of initial state distribution and policy is shown. Also, under the hypothesis of Doeblin, the functional characterization of a constrained optimal policy is obtained.

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