Call admission control and routing in integrated services networks using reinforcement learning

In integrated services 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, together with a decomposition approach, to find call admission control and routing policies. We compare the performance of our policies with a commonly used heuristic policy