Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems
On the map: Deep reinforcement learning methods → RL theory and sample complexity
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.
Key facts. Cited by 2 papers in the map (data through 13 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.
Builds on
5 of 25 references.
References · 5
- 2017Proximal Policy Optimization Algorithmscited by 14,592
- 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
- 2018Deep Reinforcement Learning for Event-Triggered Controlcited by 16
- 2022Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systemscited by 2
- 2022Data-driven Self-triggered Control via Trajectory Predictioncited by 2
Built on by
Citing papers per year: 2025: 2.
2 papers.