# Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems

arXiv: https://arxiv.org/abs/2409.19769 · HTML: https://mlsanity.com/paper/2409.19769/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [Deep Reinforcement Learning for Event-Triggered Control](https://mlsanity.com/paper/1809.05152/) (2018)
- [Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systems](https://mlsanity.com/paper/2202.08019/) (2022)
- [Data-driven Self-triggered Control via Trajectory Prediction](https://mlsanity.com/paper/2207.08596/) (2022)

## Built on by (most cited first, 2 of 2)

- [Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics](https://mlsanity.com/paper/2507.18949/) (2025)
- [Adaptive Event-Triggered Policy Gradient for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.20338/) (2025)

## Abstract (arXiv)

In this paper, we propose an adaptive event-triggered reinforcement learning control for continuous-time nonlinear systems, subject to bounded uncertainties, characterized by complex interactions. Specifically, the proposed method is capable of jointly learning both the control policy and the communication policy, thereby reducing the number of parameters and computational overhead when learning them separately or only one of them. By augmenting the state space with accrued rewards that represent the performance over the entire trajectory, we show that accurate and efficient determination of triggering conditions is possible without the need for explicit learning triggering conditions, thereby leading to an adaptive non-stationary policy. Finally, we provide several numerical examples to demonstrate the effectiveness of the proposed approach.