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Simulation-based optimization of Markov reward processes

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2 Author(s)
Marbach, P. ; Center for Commun. Syst. Res., Cambridge Univ., UK ; Tsitsiklis, J.N.

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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Automatic Control, IEEE Transactions on  (Volume:46 ,  Issue: 2 )