1 citations · 1 across the 4 of their papers we have counts for
4 papers
Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems
Vincent Bezold, Patrick Wagner, Jakob Hofmann +2
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-efficien…
Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization
Philipp Wagner, Tobias Nagel, Philipp Leube +1
Correctly setting the parameters of a production machine is essential to improve product quality, increase efficiency, and reduce production costs while also supporting sustainabil…
Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning
Tobias Nagel, Marco F. Huber
The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governi…
auto-sktime: Automated Time Series Forecasting
Marc-André Zöller, Marius Lindauer, Marco F. Huber
In today's data-driven landscape, time series forecasting is pivotal in decision-making across various sectors. Yet, the proliferation of more diverse time series data, coupled wit…