Deep Reinforcement Learning for Event-Triggered Control
This work addresses control efficiency for systems requiring reduced sampling, offering a novel approach that is incremental in combining DRL with ETC.
The paper tackles the problem of event-triggered control (ETC) by applying deep reinforcement learning (DRL) to simultaneously learn control and communication behavior from scratch, demonstrating that it can be straightforwardly applied to nonlinear systems and validated on multiple control tasks.
Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods. These frameworks are often based on a mathematical model of the system and specific designs of controller and event trigger. In this paper, we show how deep reinforcement learning (DRL) algorithms can be leveraged to simultaneously learn control and communication behavior from scratch, and present a DRL approach that is particularly suitable for ETC. To our knowledge, this is the first work to apply DRL to ETC. We validate the approach on multiple control tasks and compare it to model-based event-triggering frameworks. In particular, we demonstrate that it can, other than many model-based ETC designs, be straightforwardly applied to nonlinear systems.