Master Thesis Combining Imitation & Reinforcement Learning to Solve Automated Driving

Job description

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. The Robert Bosch GmbH is looking forward to your application! Job Description: Imitation Learning (IL) and Reinforcement Learning (RL) each come with distinct strengths and weaknesses. IL is typically sample-efficient and straightforward to implement, but it requires large amounts of expert data and often suffers from distributional shift. In contrast, RL does not rely on expert demonstrations and can learn robust policies through interaction, but it faces challenges such as unstable training dynamics and the difficulty of designing appropriate reward functions. IL has since long been applied to the problem of autonomus driving. Since recently, also RL is getting more traction, among others due to the availability of extremely fast simulators and large-scale compute. Goal of this thesis is to investigate the emergent topic of combining both approaches. - During your thesis, you will conduct in-...