Routing using Safe Reinforcement Learning

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

93 Downloads (Pure)

Abstract

The ever increasing number of connected devices has lead to a metoric rise in the amount data to be processed. This has caused computation to be moved to the edge of the cloud increasing the importance of efficiency in the whole of cloud. The use of this fog computing for time-critical control applications is on the rise and requires robust guarantees on transmission times of the packets in the network while reducing total transmission times of the various packets.

We consider networks in which the transmission times that may vary due to mobility of devices, congestion and similar artifacts. We assume knowledge of the worst case tranmssion times over each link and evaluate the typical tranmssion times through exploration. We present the use of reinforcement learning to find optimal paths through the network while never violating preset deadlines. We show that with appropriate domain knowledge, using popular reinforcement learning techniques is a promising prospect even in time-critical applications.
Original languageEnglish
Title of host publication2nd Workshop on Fog Computing and the Internet of Things
Publication statusAccepted/In press - 2020 Feb 20
Event2nd Workshop on Fog Computing and the Internet of Things -
Duration: 2020 Apr 21 → …

Workshop

Workshop2nd Workshop on Fog Computing and the Internet of Things
Abbreviated titleFog-IoT 2020
Period2020/04/21 → …

Subject classification (UKÄ)

  • Control Engineering

Fingerprint

Dive into the research topics of 'Routing using Safe Reinforcement Learning'. Together they form a unique fingerprint.

Cite this