In recent years, deep reinforcement learning algorithms have shown astonishing performance on a range of challenging control tasks. Unfortunately, such results typically require cumbersome fine-tuning of many hyperparameters, which often proves to be notoriously difficult and time-consuming. Currently I'm interested in understanding the cause for this lack of robustness and in devising improved algorithms which produce more stable results. Before joining the Bosch Center for Artificial Intelligence, I received bachelor's degrees in Electrical Engineering and Physics from University of Ulm and a master's degree in Theoretical and Mathematical Physics from LMU and TU Munich.
My research interests include (deep) reinforcement learning, policy search algorithms, Bayesian optimization, and probabilistic modeling.
My Focus Topics
- Reinforcement Learning
- Policy Search Algorithms
- Bayesian Optimization