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Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
Models of many real-life applications, such as queuing models of communication networks or computing systems, have a countably infinite state-space.
Learning a Discrete Set of Optimal Allocation Rules in a Queueing System with Unknown Service Rate
Our goal is to design a dispatching policy that maximizes the long-term average reward for the dispatcher based on observing only the arrival times and the state of the system at each arrival that reflects a realistic sampling of such systems.
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