Trustworthy and explainable deep reinforcement learning for safe and energy-efficient process control: A use case in industrial compressed air systems

This paper presents a trustworthy reinforcement learning approach for the control of industrial compressed air systems. We develop a framework that enables safe and energy-efficient operation under realistic boundary conditions and introduce a multi-level explainability pipeline combining input pert...

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Hlavní autoři:	Vincent Bezold, Patrick Wagner, Jakob Hofmann, Marco Huber, Alexander Sauer
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2026-05-01
Edice:	Energy and AI
Témata:	Deep reinforcement learning Trustworthy artificial intelligence Explainable artificial intelligence Safe reinforcement learning Industrial process control Compressed air systems
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S266654682600011X
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