Simulation - Based Optimization of Markov
نویسنده
چکیده
We propose a simulation-based algorithm for optimizing the average reward in a 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 involves the simulation of a single sample path, and can be implemented on-line. A convergence result (with probability 1) is provided. 1This research was supported by a contract with Siemens AG, Munich, Germany, and by contract DMI-9625489 with the National Science Foundation.1
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