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 class-level quality of service in terms of both blocking and dropping probabilities. Numerical simulations show the effectiveness of the approach.
Reinforcement Learning Call Control in Variable Capacity Links / Pietrabissa, Antonio. - (2010), pp. 933-938. (Intervento presentato al convegno 18th Annual International Mediterranean Conference on Control and Automation (MED) tenutosi a Marrakech, MOROCCO nel JUN 23-25, 2010) [10.1109/med.2010.5547750].
Reinforcement Learning Call Control in Variable Capacity Links
PIETRABISSA, Antonio
2010
Abstract
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 class-level quality of service in terms of both blocking and dropping probabilities. Numerical simulations show the effectiveness of the approach.File | Dimensione | Formato | |
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