Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning
Timothy X. Brown, Hui Tong, Satinder Pal Singh · 1998
This paper examines the application of reinforcement learning to a telecommunications networking problem. The problem requires that revenue be maximized while simultaneously meeting a quality of service constraint that forbids entry into certain states. We present a general solution to this multi-criteria problem that is able to earn significantly higher revenues than alternatives. 1 Introduction A number of researchers have recently explored the application of reinforcement learning (RL) to resource allocation and admission control problems in telecommunications. e.g., channel allocation in wireless systems, network routing, and admission control in telecommunication networks [1, 6, 7, 8]. Telecom problems are attractive applications for RL research because good, simple to implement, simulation models exist for them in the engineering literature that are both widely used and results on which are trusted, because there are existing solutions to compare with, because small improvements...