Master Thesis Combining Imitation & Reinforcement Learning to Solve Automated Driving

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-depth literature research and selectively implement existing approaches in the field of reinforcement and imitation learning. • Furthermore, you will extend established methods and ideas to create innovative solutions that go beyond mere reimplementation. • In addition, you will explore the generation of multi-step models using reinforcement learning to optimize algorithms for use in vehicles. • You will combine reinforcement learning, particularly single-step models, with imitation learning to generate precise trajectories for autonomous driving functions. • Lastly, you will carefully document your research findings and prepare them for scientific publication. • Education: Master studies in the field of Natural Sciences, Computer Science with a focus on AI, or comparable, with a good GPA • Experience and Knowledge: proficient in Python and PyTorch; solid knowledge gained from lectures in Artificial Intelligence, particularly Autonomous Driving, Imitation Learning, and Reinforcement Learning; initial practical experience, ideally through internships, in these areas is preferred; initial scientific publications are advantageous • Personality and Working Practice: you are able to methodically analyze complex scientific questions and independently develop innovative solutions, while consistently seeking scientific exchange as well as communicating your results precisely • Work Routine: office attendance required • Languages: fluent in English Start: according to prior agreement Duration: 6 months Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit. You are almost finished with your Bachelor's degree and would like to gain some practical experience before embarking on your next academic adventure with a Master's degree? Then you fit in perfectly well with our PreMaster Programm! Take a look at our vacancies here. Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need further information about the job? Oliver Scheel (Functional Department) Luca Paparusso (Functional Department) Work #LikeABosch starts here: Apply now! #LI-DNI