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EnglishIn this paper we study the call admission control problem to optimize the network operators' revenue guaranteeing the quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a Semi-Markov Decision Process, and we use a model based Reinforcement Learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learns it on-line. We will show how our policy provides better solution than a classic greedy algorithm.
| Titolo: | A Reinforcement Learning Admission Control Algorithm for NGN |
| Autori: | |
| Data di pubblicazione: | 2008 |
| Abstract: | In this paper we study the call admission control problem to optimize the network operators' revenue guaranteeing the quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a Semi-Markov Decision Process, and we use a model based Reinforcement Learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learns it on-line. We will show how our policy provides better solution than a classic greedy algorithm. |
| Handle: | http://hdl.handle.net/11389/397 |
| ISBN: | 9780769533339 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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