16 citations · 73 across the 34 of their papers we have counts for
5 papers · 1 filter
Risk-Sensitive Inhibitory Control for Safe Reinforcement Learning
Armin Lederer, Erfaun Noorani, John S. Baras +1
Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently receiv…
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors
Armin Lederer, Jonas Umlauft, Sandra Hirche
Due to the increasing complexity of technical systems, accurate first principle models can often not be obtained. Supervised machine learning can mitigate this issue by inferring m…
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning
Xiaobing Dai, Armin Lederer, Zewen Yang +1
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data. Gaussian process (GP) regression is a particularly…
Koopman Kernel Regression
Petar Bevanda, Max Beier, Armin Lederer +3
Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of intere…
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
Robert Lefringhausen, Supitsana Srithasan, Armin Lederer +1
As control engineering methods are applied to increasingly complex systems, data-driven approaches for system identification appear as a promising alternative to physics-based mode…