Usually I do not share open topics by default. However, I am always looking for talented and passionate students. If you have an own idea for a master’s thesis, please drop me an e-mail (including your latest transcript of records and a motivation for the thesis).
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
- Haris Asif: Quantum Circuit Optimization via Hierarchical Reinforcement Learning (FAU Erlangen-Nürnberg, 2024)
- Thorsten Bescher: Schedule-Net and Gumbel-Alpha-Zero Play-to-Plan: a Hybrid Approach for the Job-Shop Scheduling Problem (FAU Erlangen-Nürnberg, 2024)
- Stephan Geisler: Designing and training foundation models for positioning applications (FAU Erlangen-Nürnberg, 2024)
- Nicolas Kolbenschlag: Automated Generation of Block-Encodings for Quantum Policy Iteration (FAU Erlangen-Nürnberg, 2024)
- Hyeyoung Park: Interpretable Decision Tree Extraction using Imitation Learning and Locally Adaptive Optimization (LMU München, 2022)
- Lars Ulrich: Analyzing the Effect of Design Choices in Model-based Reinforcement Learning (FAU Erlangen-Nürnberg, 2022)
- Dinesh Parthasarathy: Safe Monte Carlo Tree Search using Learned Safety Critics (FAU Erlangen-Nürnberg, 2022)
- Lukas Frieß: Model-based Reinforcement Learning with First-Principle Models (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)
- Ilona Bamiller: Benchmarking Offline Reinforcement Learning on an Autonomous Driving Application (LMU München, 2021)
- 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]