2 citations · 3 across the 8 of their papers we have counts for
8 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 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…
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…
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors
Emma Cramer, Bernd Frauenknecht, Ramil Sabirov +1
Combining Reinforcement Learning (RL) with a prior controller can yield the best out of two worlds: RL can solve complex nonlinear problems, while the control prior ensures safer e…
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…
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control
Bernd Frauenknecht, Tobias Ehlgen, Sebastian Trimpe
Advanced vehicle control is a fundamental building block in the development of autonomous driving systems. Reinforcement learning (RL) promises to achieve control performance super…