期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 46, 期 2, 页码 191-209出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/9.905687
关键词
Markov reward processes; simulation-based optimization; stochastic approximation
This paper proposes a simulation-based algorithm for optimizing the average reward in a finite-state Markov reward process that depends on a set of parameters. As a special case, the method applies to Markov decision processes where optimization takes place within a parametrized set of policies. The algorithm relies on the regenerative structure of finite-state Markov processes, involves the simulation of a single sample path, and can be implemented online. A convergence result (with probability 1) is provided.
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