49 citations · 53 across the 3 of their papers we have counts for
7 papers
A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis
Maria Hügle, Gabriel Kalweit, Thomas Huegle +1
Clinical data from electronic medical records, registries or trials provide a large source of information to apply machine learning methods in order to foster precision medicine, e…
Deep Inverse Q-learning with Constraints
Gabriel Kalweit, Maria Huegle, Moritz Werling +1
Popular Maximum Entropy Inverse Reinforcement Learning approaches require the computation of expected state visitation frequencies for the optimal policy under an estimate of the r…
Deep Constrained Q-learning
Gabriel Kalweit, Maria Huegle, Moritz Werling +1
In many real world applications, reinforcement learning agents have to optimize multiple objectives while following certain rules or satisfying a list of constraints. Classical met…
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video
Oier Mees, Markus Merklinger, Gabriel Kalweit +1
Key challenges for the deployment of reinforcement learning (RL) agents in the real world are the discovery, representation and reuse of skills in the absence of a reward function.…
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving
Maria Huegle, Gabriel Kalweit, Moritz Werling +1
The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high…
Composite Q-learning: Multi-scale Q-function Decomposition and Separable Optimization
Gabriel Kalweit, Maria Huegle, Joschka Boedecker
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control. Deep Q-learning, however, still suffers from p…