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In this paper, we discuss the optimization of Markov decision processes (MDPs) with parameterized policy, where the state space is partitioned and a parameter is assigned to each partition. The goal is to find the optimal parameters which maximize the long-run average performance. The traditional policy iteration is usually inapplicable to parameterized policy because the parameter tuning at different...
We consider sensor scheduling for state estimation of a scalar system over a packet-delaying network. The current measurement data can be sent over a delay-free channel if the sensor uses larger communication energy; the data will be delayed for one time step if the sensor uses less communication energy. We consider a cost function consisting of a weighted average estimation error and a weighted terminal...
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