# Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning

arXiv: https://arxiv.org/abs/2505.24572 · HTML: https://mlsanity.com/paper/2505.24572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 25 of its 82 references that are in the map.
- First posted on arXiv 30 May 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 25 of 25)

- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Data informativity: a new perspective on data-driven analysis and control](https://mlsanity.com/paper/1908.00468/) (2019)
- [Distributionally Robust Chance Constrained Data-enabled Predictive Control](https://mlsanity.com/paper/2006.01702/) (2020)
- [Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems](https://mlsanity.com/paper/2206.01866/) (2022)
- [Extended vehicle energy dataset (eVED): an enhanced large-scale dataset for deep learning on vehicle trip energy consumption](https://mlsanity.com/paper/2203.08630/) (2022)
- [Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees](https://mlsanity.com/paper/2105.07199/) (2021)
- [Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control](https://mlsanity.com/paper/2312.14788/) (2023)
- [A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance](https://mlsanity.com/paper/2403.08808/) (2024)
- [AI-Driven Diabetic Retinopathy Diagnosis Enhancement through Image Processing and Salp Swarm Algorithm-Optimized Ensemble Network](https://mlsanity.com/paper/2503.14209/) (2025)
- [Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping](https://mlsanity.com/paper/1911.12151/) (2019)
- [An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning](https://mlsanity.com/paper/2111.08472/) (2021)
- [Active learning for anti-disturbance dual control of unknown nonlinear systems](https://mlsanity.com/paper/2212.08934/) (2022)
- [Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors](https://mlsanity.com/paper/2306.01387/) (2023)
- [Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties](https://mlsanity.com/paper/2308.03727/) (2023)
- [Ensemble Control for Stochastic Systems with Asymmetric Laplace Noises](https://mlsanity.com/paper/2405.09973/) (2024)
- [Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/2410.15837/) (2024)
- [DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization](https://mlsanity.com/paper/2412.06481/) (2024)
- [Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation](https://mlsanity.com/paper/2502.05069/) (2025)
- [Temperature-Driven Robust Disease Detection in Brain and Gastrointestinal Disorders via Context-Aware Adaptive Knowledge Distillation](https://mlsanity.com/paper/2505.06381/) (2025)
- [An Extended Kalman Filter for Data-enabled Predictive Control](https://mlsanity.com/paper/2003.08269/) (2020)
- [Data-driven predictive control with improved performance using segmented trajectories](https://mlsanity.com/paper/2108.10753/) (2021)
- [On the equivalence of direct and indirect data-driven predictive control approaches](https://mlsanity.com/paper/2403.05860/) (2024)
- [Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation](https://mlsanity.com/paper/2412.11882/) (2024)
- [Hybrid Data-enabled Predictive Control: Incorporating model knowledge into the DeePC](https://mlsanity.com/paper/2502.12467/) (2025)
- [Adaptive Fault-tolerant Control of Underwater Vehicles with Thruster Failures](https://mlsanity.com/paper/2504.16037/) (2025)

## Abstract (arXiv)

Data-enabled predictive control (DeePC) leverages system measurements in characterizing system dynamics for optimal control. The performance of DeePC relies on optimizing its hyperparameters, especially in noisy systems where the optimal hyperparameters adapt over time. Existing hyperparameter tuning approaches for DeePC are more than often computationally inefficient or overly conservative. This paper proposes an adaptive DeePC where we guide its hyperparameters adaption through reinforcement learning. We start with establishing the relationship between the system I/O behavior and DeePC hyperparameters. Then we formulate the hyperparameter tuning as a sequential decision-making problem, and we address the decision-making through reinforcement learning. We implement offline training to gain a reinforcement learning model, and we integrate the trained model with DeePC to adjust its hyperparameters adaptively in real time. We conduct numerical simulations with diverse noisy conditions, and the results demonstrate the identification of near-optimal hyperparameters and the robustness of the proposed approach against noises in the control.