The Asymptotic Behavior of Undiscounted Value Iteration in Markov Decision Problems

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Abstract
This paper considers undiscounted Markov Decision Problems. For the general multichain case, we obtain necessary and sufficient conditions which guarantee that the maximal total expected reward for a planning horizon of n epochs minus n times the long run average expected reward has a finite limit as n → ∞ for each initial state and each final reward vector. In addition, we obtain a characterization of the chain and periodicity structure of the set of one-step and J-step maximal gain policies. Finally, we discuss the asymptotic properties of the undiscounted value-iteration method.

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