Cover Image

Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bezold, Vincent, Wagner, Patrick, Hofmann, Jakob, Huber, Marco, Sauer, Alexander
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market
by: Bezold, Vincent, et al.
Published: (2025)

Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control
by: Schäfer, Georg, et al.
Published: (2025)

On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
by: Andres, Alain, et al.
Published: (2024)

Safe Reinforcement Learning for Real-World Engine Control
by: Bedei, Julian, et al.
Published: (2025)

Safe Reinforcement Learning for Real-World Engine Control Data and Scripts
by: Bedei, Julian, et al.
Published: (2024)

Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
by: Kuznietsov, Anton, et al.
Published: (2024)

Large Language Models as Explainable Cyberattack Detectors for Energy Industrial Control Systems
by: Kong, Weiyi, et al.
Published: (2026)

Ester – eine Gewaltgeschichte
by: Bezold, Helge
Published: (2023)

From Theory to Practice: Real-World Use Cases on Trustworthy LLM-Driven Process Modeling, Prediction and Automation
by: Pfeiffer, Peter, et al.
Published: (2025)

Safe Reinforcement Learning-based Control for Hydrogen Diesel Dual-Fuel Engines
by: Sharma, Vasu, et al.
Published: (2025)

Robustness of Explainable Artificial Intelligence in Industrial Process Modelling
by: Kantz, Benedikt, et al.
Published: (2024)

Deployment Challenges of Industrial Intrusion Detection Systems
by: Wolsing, Konrad, et al.
Published: (2024)

Systematic Analysis of Penalty-Optimised Illumination Design for Tomographic Volumetric Additive Manufacturing via the Extendable Framework TVAM AID Using the Core Imaging Library
by: Pellizzon, Nicole, et al.
Published: (2026)

Trustworthy AI: UK Air Traffic Control Revisited
by: Procter, Rob, et al.
Published: (2025)

Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models
by: Olumuyiwa, Badaru I., et al.
Published: (2024)

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
by: Hong, Junyuan, et al.
Published: (2024)

Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domain
by: Huber, Marco, et al.
Published: (2024)

Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case
by: Roy, Swastika, et al.
Published: (2023)

A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
by: Koursioumpas, Nikolaos, et al.
Published: (2023)

Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks
by: Huber, Stefan, et al.
Published: (2026)

Fast Gaussian Processes under Monotonicity Constraints
by: Zhang, Chao, et al.
Published: (2025)

Use Cases for Terahertz Communications: An Industrial Perspective
by: Zugno, Tommaso, et al.
Published: (2025)

Trustworthy Distributed Usage Control Enforcement in Heterogeneous Trusted Computing Environments
by: Wagner, Paul Georg
Published: (2025)

Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty
by: Hill, Vincent W.
Published: (2024)

DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning
by: Wang, Xuefeng, et al.
Published: (2024)

Safe Obstacle-Free Guidance of Space Manipulators in Debris Removal Missions via Deep Reinforcement Learning
by: Lam, Vincent, et al.
Published: (2025)

When Is It Safe to Fly? Early Air Travel After Small Traumatic Pneumothorax
by: Arabella T. Patrick, et al.
Published: (2026)

Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency
by: Wang, Xijun, et al.
Published: (2024)

Identification of Pressure‐Swing Separation Processes for Azeotropic Mixtures Using Deep Reinforcement Learning
by: Alexander B. Wolf, et al.
Published: (2025)

Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
by: Esfahani, Hossein Nejatbakhsh, et al.
Published: (2025)

Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
by: Langschwert, Julian, et al.
Published: (2026)

Research on the Construction Process Scheme of Artificial Chamber of Compressed Air Energy Storage Power Station
by: Bei Cai, et al.
Published: (2025)

Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control
by: Mohaya, Turki Bin, et al.
Published: (2026)

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems
by: Lan, Guangchen
Published: (2026)

Reinforcing Trustworthiness in Multimodal Emotional Support Systems
by: Le, Huy M., et al.
Published: (2025)

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems
by: Amar Singh, et al.
Published: (2024)

Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers
by: Henkel, Vincent, et al.
Published: (2024)

TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
by: Liu, Hui, et al.
Published: (2024)

LLM-Guided Safe Reinforcement Learning for Energy System Topology Reconfiguration
by: Zhang, Zongyan, et al.
Published: (2026)

Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization
by: Wagner, Philipp, et al.
Published: (2025)