A reinforcement learning approach to call admission and call dropping control in links with variable capacity

This paper defines a reinforcement learning (RL) approach to call control algorithms in links with variable capacity supporting multiple classes of service. The novelties of the document are the following: i) the problem is modeled as a constrained Markov decision process (MDP); ii) the constrained MDP is solved via a RL algorithm by using the Lagrangian approach and state aggregation. The proposed approach is capable of controlling classlevel quality of service in terms of both blocking and dropping probabilities. Numerical simulations show the effectiveness of the approach. © 2011 EUCA.