DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling
This addresses scheduling issues in cyber-physical systems like IoT, but it is incremental as it builds on existing control and learning methods.
The paper tackles the problem of scheduling communication in networked control systems with limited network capacity, proposing DeepCAS, a deep reinforcement learning algorithm that minimizes control loss and outperforms periodic scheduling.
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 \emph{control-aware} scheduling (\textsc{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 \emph{control loss}. We present empirical results to show that \textsc{DeepCAS} finds schedules with better performance than periodic ones.