mlsanityData to 14 Oct 2026

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

arXiv 2512.15735 ↗First posted 5 Dec 2025math.OC cs.AI cs.SYcited by 3

On the map: Deep reinforcement learning methods → RL theory and sample complexity

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.

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.

Builds on

5 of 33 references.

References · 5

  1. 2024Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approachcited by 7
  2. 2013Concurrent learning-based approximate optimal regulationcited by 5
  3. 2025Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systemscited by 4
  4. 2024Discrete-Time Stress Matrix-Based Formation Control of General Linear Multi-Agent Systemscited by 3
  5. 2025A temporal scale transformer framework for precise remaining useful life prediction in fuel cellscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policiescited by 4
  2. 2026SignVLA: A Gloss-Free Vision-Language-Action Framework for Real-Time Sign Language-Guided Robotic Manipulationcited by 3
  3. 2026SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems
  2. 2024Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty
  3. 2023Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control
  4. 2025Improved Robustness of Deep Reinforcement Learning for Control of Time-Varying Systems by Bounded Extremum Seeking
  5. 2024Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
  6. 2026Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation
  7. 2024Learning Robust Policies via Interpretable Hamilton-Jacobi Reachability-Guided Disturbances
  8. 2026Predefined-Time Resilient Integral Reinforcement Learning for Input-Constrained Unknown Nonlinear Systems Under FDI Attacks and Disturbances: A Fully Data-Driven Approach