Routing using Safe Reinforcement Learning

Summary, in English

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.

• Author: Gautham Nayak Seetanadi; Karl-Erik Årzén
• Department/s: Department of Automatic Control; ELLIIT: the Linköping-Lund initiative on IT and mobile communication
• Publishing year: 2020-02-20
• Language: English
• Publication/Series: 2nd Workshop on Fog Computing and the Internet of Things
• Full text: - 451 kB; Download statistics
• Document type: Conference paper
• Topic: Control Engineering
• Conference name: 2nd Workshop on Fog Computing and the Internet of Things
• Conference date: 2020-04-21
• Status: Inpress
• Project: ELLIIT LU P02: Co-Design of Robust and Secure Networked Embedded Control Systems
• ISBN/ISSN/Other: ISBN: 978-395977144-3