Reinforcement learning for call admission control and routing under quality of service constraints in multimedia networks

In this paper, we solve the call admission control and routing problem in multimedia networks via reinforcement learning (RL). The problem requires that network revenue be maximized while simultaneously meeting quality of service constraints that forbid entry into certain states and use of certain actions. The problem can be formulated as a constrained semi-Markov decision process. We show that RL provides a solution to this problem and is able to earn significantly higher revenues than alternative heuristics.