Denumerable State Nonhomogeneous Markov Decision Processes
نویسنده
چکیده
We consider denumerable state nonhomogeneous Markov decision processes and extend results from both denumerable state homogeneous and finite state nonhomogeneous problems. We show that, under weak ergodicity, accumulation points of finite horizon optima (termed algorithmic optima) are average cost optimal. We also establish the existence of solution horizons. Finally, an algorithm is presented to solve problems of this class for the case where there is a unique algorithmic optimum.
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