  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.
