@inproceedings{9df14107c7534254adf6d6e9611338f5,
title = "Applying reinforcement learning to basic routing problem",
abstract = "Routing jobs to parallel servers is a common and important task in today{\textquoteright}s computer and communication systems. As each routing decision affects the jobs arriving later, determining the (near) optimal decisions is non-trivial. In this paper, we apply reinforcement learning techniques to the job routing problem with heterogeneous servers and a general cost structure. We study the convergence of the reinforcement learning to a near-optimal policy (that we can determine by other means), and compare its performance against heuristic policies such as Join-the-Shortest-Queue (JSQ) and Shortest-Expected-Delay (SED).",
keywords = "Job dispatching, Machine learning, Parallel servers, Reinforcement learning, Task assignment, Value function",
author = "Sam{\'u}elsson, \{Sigur{\dh}ur Gauti\} and Esa Hyyti{\"a}",
note = "Funding Information: Acknowledgements. This work was supported by the Academy of Finland in the FQ4BD project (grant no. 296206) and by the University of Iceland Research Fund in the RL-STAR project. Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 13th International Conference on Queueing Theory and Network Applications, QTNA 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
doi = "10.1007/978-3-319-93736-6\_18",
language = "English",
isbn = "9783319937359",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "238--249",
editor = "Wuyi Yue and Sabine Wittevrongel and Yutaka Takahashi and Tuan Phung-Duc",
booktitle = "Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings",
address = "Germany",
}