mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for Event-Triggered Control

arXiv 1809.05152 ↗First posted 13 Sep 2018eess.SY cs.LG cs.SYcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2018.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 1; 2024: 3; 2025: 3; 2026: 2.

16 papers.

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  1. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
  2. 2022Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approachcited by 7
  3. 2022Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Drivingcited by 6
  4. 2024Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learningcited by 6
  5. 2021Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Controlcited by 4
  6. 2021Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transportcited by 3
  7. 2022Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuverscited by 3
  8. 2025Privileged observations enable rapid and reliable policy discovery directly in the physical worldcited by 3
  9. 2023Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systemscited by 2
  10. 2024Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systemscited by 2
  11. 2021Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specificationscited by 1
  12. 2026Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurancecited by 1
4 more
  1. 2024Communication-Control Codesign for Large-Scale Wireless Networked Control Systemscited by 0
  2. 2025Adaptive Event-Triggered Policy Gradient for Multi-Agent Reinforcement Learningcited by 0
  3. 2025Solve Smart, Not Often: Policy Learning for Costly MILP Re-solvingcited by 0
  4. 2026Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learningcited by 0