18 citations · 27 across the 7 of their papers we have counts for
8 papers
Quantile Regression for Distributional Reward Models in RLHF
Nicolai Dorka
Reinforcement learning from human feedback (RLHF) has become a key method for aligning large language models (LLMs) with human preferences through the use of reward models. However…
Training a Vision Language Model as Smartphone Assistant
Nicolai Dorka, Janusz Marecki, Ammar Anwar
Addressing the challenge of a digital assistant capable of executing a wide array of user tasks, our research focuses on the realm of instruction-based mobile device control. We le…
Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces
Johan Vertens, Nicolai Dorka, Tim Welschehold +2
The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a…
Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
Nicolai Dorka, Tim Welschehold, Wolfram Burgard
Early stopping based on the validation set performance is a popular approach to find the right balance between under- and overfitting in the context of supervised learning. However…
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
Nicolai Dorka, Tim Welschehold, Joschka Boedecker +1
Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on temporal difference learning typically are prone to an over- or underestimation bi…
Modality-Buffet for Real-Time Object Detection
Nicolai Dorka, Johannes Meyer, Wolfram Burgard
Real-time object detection in videos using lightweight hardware is a crucial component of many robotic tasks. Detectors using different modalities and with varying computational co…
Scaling Imitation Learning in Minecraft
Artemij Amiranashvili, Nicolai Dorka, Wolfram Burgard +2
Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments. We apply imitation learning to attain state-of-the-art perfo…
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
Jingwei Zhang, Niklas Wetzel, Nicolai Dorka +2
Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward sign…