Reinforcement Learning for Call Admission Control and Routing in Integrated Service Networks
Peter Marbach, Oliver Mihatsch, Miriam Schulte, John N. Tsitsiklis · 1997
In integrated service communication networks, an important problem is to exercise call admission control and routing so as to optimally use the network resources. This problem is naturally formulated as a dynamic programming problem, which, however, is too complex to be solved exactly. We use methods of reinforcement learning (RL), together with a decomposition approach, to find call admission control and routing policies. The performance of our policy for a network with approximately 10 45 different feature configurations is compared with a commonly used heuristic policy. 1 Introduction The call admission control and routing problem arises in the context where a telecommunication provider wants to sell its network resources to customers in order to maximize long term revenue. Customers are divided into different classes, called service types. Each Author to whom correspondence should be addressed. service type is characterized by its bandwidth demand, its average call holding...