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

被引：15
|
作者：
Tong, H [1 ]
Brown, TX [1 ]
机构：
[1] Univ Colorado, Boulder, CO 80309 USA
来源：
MACHINE LEARNING | 2002年 / 49卷 / 2-3期
基金：
美国国家科学基金会;
关键词：
reinforcement learning; call admission control; routing; quality of service; multimedia networks;
D O I：
10.1023/A:1017924227920
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：111 / 139
页数：29
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