no code implementations • 7 Aug 2019 • Jörg Wagner, Jan Mathias Köhler, Tobias Gindele, Leon Hetzel, Jakob Thaddäus Wiedemer, Sven Behnke
Our approach is based on a novel technique to defend against adversarial evidence (i. e. faulty evidence due to artefacts) by filtering gradients during optimization.
no code implementations • 13 Apr 2016 • Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent.