42 citations · 138 across the 22 of their papers we have counts for
12 papers · 1 filter
All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning
Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele +2
Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data effic…
Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty
Artur Eisele, Bernd Frauenknecht, Friedrich Solowjow +1
Safety remains an open problem in reinforcement learning (RL), especially during training. While safety filters are promising to address safe exploration, they are generally poorly…
Kernel conditional tests from learning-theoretic bounds
Pierre-François Massiani, Christian Fiedler, Lukas Haverbeck +2
We propose a framework for hypothesis testing on conditional probability distributions, which we then use to construct statistical tests of functionals of conditional distributions…
On Rollouts in Model-Based Reinforcement Learning
Bernd Frauenknecht, Devdutt Subhasish, Friedrich Solowjow +1
Model-based reinforcement learning (MBRL) seeks to enhance data efficiency by learning a model of the environment and generating synthetic rollouts from it. However, accumulated mo…
On Foundation Models for Dynamical Systems from Purely Synthetic Data
Martin Ziegler, Andres Felipe Posada-Moreno, Friedrich Solowjow +1
Foundation models have demonstrated remarkable generalization, data efficiency, and robustness properties across various domains. In this paper, we explore the feasibility of found…
Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
Bernd Frauenknecht, Artur Eisele, Devdutt Subhasish +2
Dyna-style model-based reinforcement learning (MBRL) combines model-free agents with predictive transition models through model-based rollouts. This combination raises a critical q…
On the Consistency of Kernel Methods with Dependent Observations
Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow
The consistency of a learning method is usually established under the assumption that the observations are a realization of an independent and identically distributed (i.i.d.) or m…
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
Behnam Khojasteh, Friedrich Solowjow, Sebastian Trimpe +1
Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expert…
Event-Triggered Time-Varying Bayesian Optimization
Paul Brunzema, Alexander von Rohr, Friedrich Solowjow +1
We consider the problem of sequentially optimizing a time-varying objective function using time-varying Bayesian optimization (TVBO). Current approaches to TVBO require prior knowl…
Structure-preserving Gaussian Process Dynamics
Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche +2
Most physical processes posses structural properties such as constant energies, volumes, and other invariants over time. When learning models of such dynamical systems, it is criti…
Identifying Causal Structure in Dynamical Systems
Dominik Baumann, Friedrich Solowjow, Karl H. Johansson +1
Mathematical models are fundamental building blocks in the design of dynamical control systems. As control systems are becoming increasingly complex and networked, approaches for o…
Actively Learning Gaussian Process Dynamics
Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient…