2 citations · 3 across the 8 of their papers we have counts for
4 papers · 2 filters
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 is a key problem in reinforcement learning, especially during exploration. While safety filters are promising to address this issue, they are ill-suited for high- dimensiona…
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank +2
Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on fir…
Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Dynamics Models
Julia Berger, Bernd Frauenknecht, Sebastian Trimpe +1
Model-based reinforcement learning distinguishes between dynamics models operating on proprioceptive states and latent dynamics models typically operating on high-dimensional image…