期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 55, 期 1, 页码 201-207出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2009.2033848
关键词
Approximation of control problems; Ergodic Markov decision processes (MDPs); policy iteration algorithm
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.
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