License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.Fog-IoT.2020.6
URN: urn:nbn:de:0030-drops-120007
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Nayak Seetanadi, Gautham ; Årzén, Karl-Erik

Routing Using Safe Reinforcement Learning

OASIcs-Fog-IoT-2020-6.pdf (0.4 MB)


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.

BibTeX - Entry

  author =	{Gautham Nayak Seetanadi and Karl-Erik Årz{\'e}n},
  title =	{{Routing Using Safe Reinforcement Learning}},
  booktitle =	{2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)},
  pages =	{6:1--6:8},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-144-3},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{80},
  editor =	{Anton Cervin and Yang Yang},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-120007},
  doi =		{10.4230/OASIcs.Fog-IoT.2020.6},
  annote =	{Keywords: Real time routing, safe exploration, safe reinforcement learning, time-critical systems, dynamic routing}

Keywords: Real time routing, safe exploration, safe reinforcement learning, time-critical systems, dynamic routing
Collection: 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)
Issue Date: 2020
Date of publication: 08.04.2020

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