Besides open topics listed below we are always looking for talented and passionate students and employees (student assistants). Please find more information on our open thesis below – if you have an own idea, please also feel free to drop me an e-mail.
Open Thesis
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Meta Reinforcement Learning for Optimization of Electric Circuit Parameters
The design and optimization of electric circuits is currently still an experience driven approach. Especially in the case of resonant systems, the strong non-linear system behavior requires a lot of optimization loops during the design process to maximize power transfer and efficiency. A simple example of the above systems is the boost converter. Recently, methods using genetic algorithms (GAs) like…Continue reading »
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Multi-Agent Reinforcement Learning for the Coordination of Base Stations in 6G Networks
6G technology is promising to fundamentally change how consumers and businesses communicate, based on its envisioned speed and flexibility. This flexibility stems from the complex interplay between large-scale ecosystems of software and hardware network components and renders classical theoretical approaches unable to seamlessly scale to the massive problem size [1]. Reinforcement learning [2] can provide a viable approach to alleviate…Continue reading »
Running Thesis
Finished Thesis
- 2022: Lars Ulrich: Analyzing the Effect of Design Choices in Model-based Reinforcement Learning [FAU Erlangen-Nürnberg]
- 2022: Hyeyoung Park: Interpretable Decision Tree Extraction using Imitation Learning and Locally Adaptive Optimization [LMU München]
- 2022: Dinesh Parthasarathy: Safe Monte Carlo Tree Search using Learned Safety Critics [FAU Erlangen-Nürnberg]
- 2021: Sebastian Fischer: Back to the Basics: Offline Reinforcement Learning with Least-Squares Methods for Policy Iteration [LMU München]
- 2021: Matthias Gruber: Learning to Avoid your Supervisor [LMU München]
- 2021: Lukas Frieß: Model-based Reinforcement Learning with First-Principle Models [FAU Erlangen-Nürnberg]
- 2021: Ilona Bamiller: Benchmarking Offline Reinforcement Learning on an Autonomous Driving Application [LMU München]
- 2020: Daniel Landgraf: Hierarchical Learning and Model Predictive Control [FAU Erlangen-Nürnberg]
- David Kießling: Real-Time Non-Linear Model Predictive Control for Collision Avoidance of Vehicles [Master Thesis @ FAU]
- Leonid Butyrev: Overcoming Catastrophic Forgetting in Deep Reinforcement Learning [Master Thesis @ FAU]
- Andreas Mühlroth: Transparent and Interpretable Reinforcement Learning [Master Thesis @ FAU]
- Tim Nisslbeck: Learning a Car Driving Simulator to enable Deep Reinforcement Learning [Master Thesis @ FAU]
- Leonid Butyrev: Exploiting Reinforcement Learning for Complex Trajectory Planning for Mobile Robots [Bachelor Thesis @ FAU]