The Effect of Recurrent Networks on Policy Improvement in Polling Systems
This paper considers polling policies represented by recurrent neural networks and investigates the effect of feedback weights on the optimality. The polling system consists of a single server and multiple stations and whenever the server finishes serving one of the stations, it determines the next station to visit according to the output of the neural network for the current system state. By using the simulated annealing method, we improve the polling policy in such a way that the mean delay of customers is to be minimized in the steady state. The benefit of applying recurrent networks is in that they can represent a broader class of policies than feedforward networks. Numerical results show that recurrent networks can substantially reduce the (sub-)optimal mean delay in comparison with feedforward networks.
KeywordsHide Layer Queue Length Polling System Feedforward Network Recurrent Network
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