[Master Thesis with Bosch R&D] Bridging the Gap between Reinforcement Learning & End-to-End Driving

11.05.2026, Diplomarbeiten, Bachelor- und Masterarbeiten

This thesis investigates the integration of Reinforcement Learning (RL) with end-to-end (E2E) autonomous driving approaches. While E2E methods rely on large amounts of expert data, RL enables learning through interaction in simulation. The goal is to explore how RL-based simulation and feedback mechanisms can improve the robustness and performance of state-of-the-art E2E driving policies.

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 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 acting based on feedback.

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

>During your Master thesis, you will collaborate with a team of engineers and researchers to bridge the gap between RL simulation and training, and E2E driving.
>Furthermore you will understand the fundamental properties behind different training strategies and use them to guide the development of novel models and policies.
>You will engineer and contribute efficient and high-performance software.
>In Addition you will conduct experiments and analyze data to identify areas for improvement and optimize model accuracy and reliability.
>You will stay up to date with the latest advancements in autonomous driving technology and contribute innovative ideas to the team.
>Finally, you will document findings and present results in a publishable manner as well as work on open-source benchmarks and datasets.

Qualifications

>Education: Master studies in the field of Computer Science, Electrical Engineering or comparable 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

Additional Information

>Start: according to prior agreement
>Duration: 6 months
>Registered as: TUM Student
>Place: Robert-Bosch-Campus 1, 71272 Renningen, Germany

If you are interested, please send a short motivation letter, CV, and transcript of records to: yuan_avs.gao@tum.de

Kontakt: yuan_avs.gao@tum.de