论文信息 - Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning

Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning

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.

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