We consider the problem of Safe Deep Reinforcement Learning (DRL) using formal
verification in a trajectory generation task. In more detail, we propose an approach to verify …

With the rapidly advancing improvements to the already successful Deep Learning artifacts,
Neural Networks (NN) are poised to permeate a growing number of everyday applications …

Abstract A Deep Reinforcement Learning (DRL) agent tries to learn a policy maximizing a
long-term objective by trials and errors in large state spaces. However, this learning …

Autonom agierende Agenten handeln zunehmend in räumlicher Nähe zu Menschen,
wodurch das sichere und ethisch korrekte Verhalten der Agenten immer wichtiger wird. Um …