# Deep Reinforcement Learning for Event-Triggered Control

arXiv: https://arxiv.org/abs/1809.05152 · HTML: https://mlsanity.com/paper/1809.05152/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2208.10302/) (2022)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learning](https://mlsanity.com/paper/2402.00046/) (2024)
- [Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport](https://mlsanity.com/paper/2103.15260/) (2021)
- [Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control](https://mlsanity.com/paper/2109.12562/) (2021)
- [Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers](https://mlsanity.com/paper/2209.11043/) (2022)
- [Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2311.16895/) (2023)
- [Privileged observations enable rapid and reliable policy discovery directly in the physical world](https://mlsanity.com/paper/2512.08463/) (2025)
- [Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems](https://mlsanity.com/paper/2409.19769/) (2024)
- [Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specifications](https://mlsanity.com/paper/2108.01317/) (2021)
- [Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving](https://mlsanity.com/paper/2509.23470/) (2025)
- [Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance](https://mlsanity.com/paper/2605.12561/) (2026)
- [Communication-Control Codesign for Large-Scale Wireless Networked Control Systems](https://mlsanity.com/paper/2410.11316/) (2024)
- [Adaptive Event-Triggered Policy Gradient for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.20338/) (2025)
- [Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning](https://mlsanity.com/paper/2605.10482/) (2026)

## 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.