DeepAI AI Chat
Log In Sign Up

Deep Reinforcement Learning for Event-Triggered Control

by   Dominik Baumann, et al.

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.


page 1

page 2

page 3

page 4


Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters

Accurate control of autonomous marine robots still poses challenges due ...

Deep Reinforcement Learning for Electric Transmission Voltage Control

Today, human operators primarily perform voltage control of the electric...

High Performance Across Two Atari Paddle Games Using the Same Perceptual Control Architecture Without Training

Deep reinforcement learning (DRL) requires large samples and a long trai...

Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP

The bottleneck of distributed edge learning (DEL) over wireless has shif...

GMI-DRL: Empowering Multi-GPU Deep Reinforcement Learning with GPU Spatial Multiplexing

With the increasing popularity of robotics in industrial control and aut...

Co-designing Intelligent Control of Building HVACs and Microgrids

Building loads consume roughly 40 countries, a significant part of which...

TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning

High Power Laser's (HPL) optimal performance is essential for the succes...