Teaching machines to autonomously explore their environment and to learn new skills on their own is one of the big promises of machine learning, artificial intelligence and in particular reinforcement learning. In reality, autonomous behavior of current robotics systems is still painfully limited to very specific tasks in mostly controlled environments or at least environments which can be easily simulated to obtain vast amounts of data.

In his PhD work, Andreas Doerr is addressing the problems encountered when applying reinforcement learning techniques to real world systems, where data-efficient learning in the presences of noisy and incomplete measurements (POMDP) of the system’s true underlying state is required. One particular line of research is concerned with improving probabilistic, model-based reinforcement learning methods [ ] and tailoring the model learning techniques to the subsequent task of policy search [ ].

Andreas Doerr is a PhD student working in close collaboration with the Max-Planck Institute for Intelligent Systems and the Bosch Center for Artificial Intelligence. He is affiliated with the Autonomous Motion Department, supervised by and working with Dr. Sebastian Trimpe in the Intelligent Control Systems group. His PhD adviser is Prof. Toussaint from the Machine Learning & Robotics Lab (University of Stuttgart).

Prior to his PhD studies, he received a M.Sc. degree (Dipl.-Ing.) in Aerospace-Engineering and a B.Sc. degree in Computer Science, both from the University of Stuttgart.

reinforcement learning model-based reinforcement learning probabilistic models time-series dynamics models