DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling

Burak Demirel1, Arunselvan Ramaswamy2, Daniel E. Quevedo2, Holger Karl3

  • 1Scania
  • 2Paderborn University
  • 3University of Paderborn

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Regular Session

Sessions

10:00 - 12:00 | Mon 17 Dec | Glimmer 2 | MoA10

Machine Learning I

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Abstract

We consider networked control systems consisting of multiple independent controlled subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-physical systems, Internet of Things, and large-scale industrial systems. In many large-scale settings, the size of the communication network is smaller than the size of the system. In consequence, scheduling issues arise. The main contribution of this paper is to develop a deep reinforcement learning-based control-aware scheduling (DeepCAS) algorithm to tackle these issues. We use the following (optimal) design strategy: First, we synthesize an optimal controller for each subsystem; next, we design a learning algorithm that adapts to the chosen subsystems (plants) and controllers. As a consequence of this adaptation, our algorithm finds a schedule that minimizes the control loss. We present empirical results to show that DeepCAS finds schedules with better performance than periodic ones.

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