234 citations · 938 across the 65 of their papers we have counts for
6 papers · 1 filter
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways
Branka Mirchevska, Maria Hügle, Gabriel Kalweit +2
Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds. As a result, choosing the…
Deep Surrogate Q-Learning for Autonomous Driving
Maria Kalweit, Gabriel Kalweit, Moritz Werling +1
Challenging problems of deep reinforcement learning systems with regard to the application on real systems are their adaptivity to changing environments and their efficiency w.r.t.…
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…
Machine-Learning-Based Diagnostics of EEG Pathology
Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabąszcz +5
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approache…