# Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming

arXiv: https://arxiv.org/abs/2512.15735 · HTML: https://mlsanity.com/paper/2512.15735/

## Key facts

- Cited by 3 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 33 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## 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)

- [Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach](https://mlsanity.com/paper/2401.15785/) (2024)
- [Concurrent learning-based approximate optimal regulation](https://mlsanity.com/paper/1304.3477/) (2013)
- [Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2507.04100/) (2025)
- [Discrete-Time Stress Matrix-Based Formation Control of General Linear Multi-Agent Systems](https://mlsanity.com/paper/2401.05083/) (2024)
- [A temporal scale transformer framework for precise remaining useful life prediction in fuel cells](https://mlsanity.com/paper/2504.08803/) (2025)

## Built on by (most cited first, 3 of 3)

- [Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies](https://mlsanity.com/paper/2607.02092/) (2026)
- [SignVLA: A Gloss-Free Vision-Language-Action Framework for Real-Time Sign Language-Guided Robotic Manipulation](https://mlsanity.com/paper/2602.22514/) (2026)
- [SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models](https://mlsanity.com/paper/2606.20857/) (2026)

## Abstract (arXiv)

This work proposes a unified control architecture that couples a Reinforcement Learning (RL)-driven controller with a disturbance-rejection Extended State Observer (ESO), complemented by an Event-Triggered Mechanism (ETM) to limit unnecessary computations. The ESO is utilized to estimate the system states and the lumped disturbance in real time, forming the foundation for effective disturbance compensation. To obtain near-optimal behavior without an accurate system description, a value-iteration-based Adaptive Dynamic Programming (ADP) method is adopted for policy approximation. The inclusion of the ETM ensures that parameter updates of the learning module are executed only when the state deviation surpasses a predefined bound, thereby preventing excessive learning activity and substantially reducing computational load. A Lyapunov-oriented analysis is used to characterize the stability properties of the resulting closed-loop system. Numerical experiments further confirm that the developed approach maintains strong control performance and disturbance tolerance, while achieving a significant reduction in sampling and processing effort compared with standard time-triggered ADP schemes.