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Routing Using Safe Reinforcement Learning

Authors Gautham Nayak Seetanadi , Karl-Erik Årzén

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Author Details

Gautham Nayak Seetanadi
  • Department of Automatic Control, Lund University, Sweden
Karl-Erik Årzén
  • Department of Automatic Control, Lund University, Sweden

Funding

The authors are members of the LCCC Linnaeus Center and the ELLIIT Strategic Research Area at Lund University. This work was supported by the Swedish Research Council through the project "Feedback Computing", VR 621-2014-6256.

Cite AsGet BibTex

Gautham Nayak Seetanadi and Karl-Erik Årzén. Routing Using Safe Reinforcement Learning. In 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020). Open Access Series in Informatics (OASIcs), Volume 80, pp. 6:1-6:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/OASIcs.Fog-IoT.2020.6

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Reinforcement learning
  • Networks → Packet scheduling
Keywords
  • Real time routing
  • safe exploration
  • safe reinforcement learning
  • time-critical systems
  • dynamic routing

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