Optimizing Admission Control While Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning
Abstract
This paper examines the application of reinforcement learning to a telecommunications networking problem . The problem requires that rev(cid:173) enue 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.
Cite
Text
Brown et al. "Optimizing Admission Control While Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning." Neural Information Processing Systems, 1998.Markdown
[Brown et al. "Optimizing Admission Control While Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/brown1998neurips-optimizing/)BibTeX
@inproceedings{brown1998neurips-optimizing,
title = {{Optimizing Admission Control While Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning}},
author = {Brown, Timothy X. and Tong, Hui and Singh, Satinder P.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {982-988},
url = {https://mlanthology.org/neurips/1998/brown1998neurips-optimizing/}
}