Master Thesis Bridging the Gap between Reinforcement Learning & E2E Driving

Master Thesis Bridging the Gap between Reinforcement Learning & E2E Driving

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Are you passionate about the future of autonomous driving? We are seeking a talented and motivated individual to join our team of experts dedicated to advancing the capabilities of autonomous vehicles. In this role, you will play a crucial part in using Reinforcement Learning (RL) to enhance the performance of end-to-end (E2E) approaches.

The field of autonomous driving has experienced a paradigm shift with the emergence of batched RL simulation, enabling relatively cheap closed-loop training of high-performance policies that can learn from their own experience without human expert data. In contrast, E2E driving approaches rely on large amounts of rich expert data but are increasingly using RL‑like training strategies to inject the notion of experience and act based on feedback.

This thesis aims to investigate approaches to integrate and enhance state‑of‑the‑art E2E driving policies with RL simulation.

• Collaborate with a team of engineers and researchers to bridge the gap between RL simulation and training, and E2E driving.
• Understand the fundamental properties behind different training strategies and use them to guide the development of novel models and policies.
• Engineer and contribute efficient and high‑performance software.
• Conduct experiments and analyze data to identify areas for improvement and optimize model accuracy and reliability.
• Stay up to date with the latest advancements in autonomous driving technology and contribute innovative ideas to the team.
• Document findings and present results in a publishable manner as well as work on open‑source benchmarks and datasets.

• Education: Master studies in Computer Science, Electrical Engineering or comparable fields with a Robotics/Machine Learning focus and very good grades.
• Experience and Knowledge: Reading research papers and programming experience for machine learning applications, with sound knowledge in Python, PyTorch, TensorFlow or JAX.
• Personality and Working Practice: You are ready to learn a lot and dive into a topic at the frontiers of machine learning research and autonomous driving applications; in case of own novel contributions, you should be eager to publish them.
• Work Routine: Office attendance required.
• Languages: Very good 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.

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.