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Approximating Ergodic Average Reward Continuous-Time Controlled Markov Chains

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2 Author(s)
TomÁs Prieto-Rumeau ; Department of Statistics and Operations Research, UNED, Madrid, Spain ; JosÉ MarÍa Lorenzo

We study the approximation of an ergodic average reward continuous-time denumerable state Markov decision process (MDP) by means of a sequence of MDPs. Our results include the convergence of the corresponding optimal policies and the optimal gains. For a controlled upwardly skip-free process, we show some computational results to illustrate the convergence theorems.

Published in:

IEEE Transactions on Automatic Control  (Volume:55 ,  Issue: 1 )