In this paper we study the call admission control problem to optimize the network operators revenue guaranteeing quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a semi-Markov decision process, and we use a model based reinforcement learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learn it on-line. We will show how our policy provides better solution than a classic greedy algorithm.
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