Jannis Blüml
Artificial Intelligence and Machine Learning Group, Computer Science Department, TU Darmstadt and hessian.AI
S2|02, D216 Hochschulstraße 10, 64289 Darmstadt, Germany
jannis (dot) blueml (at) tu-darmstadt (dot) de
Meetings by appointment.
Mission. My research is centered around reinforcement learning, using deep neural networks. Currently I try to combine modern approaches in this field with transformer networks as well as develop new approaches. My overall goal is to understand reasoning within RL better, especially when used in partial observable enviroments.

Availability of thesis topics. If you are not yet familiar with our thesis notes, I recommend you start here: Thesis Proposals at AIML
As I said, my focus is on RL. Accordingly, theses that I supervise often fall into this area. Here it is helpful, but not mandatory, if you are familiar with PyTorch/Tensorflow and Deep Neural Networks. If you already have a topic in this direction in mind or are generally interested, feel free to contact me.

Update 2026: I do not take on any new students for theses or projects at the moment.
Comeback in October 2026.



Timeline.
2021 - now: PhD student at AIML, CS Department, TU Darmstadt, and hessian.AI, Germany
2018 - 2021: M.Sc. Computer Science at TU Darmstadt, Germany
2014 - 2018: B.Sc. Computer Science at TU Darmstadt, Germany

Supervised Theses.
2026 Improving Input Representations in Object-Centric Deep Reinforcment Learning, M.Sc. Thesis
2026 LLM-Assisted Reward Shaping In Object-Centric Environments, B.Sc. Thesis
2026 Structured World Understanding: Integrating Object-Centric Learning with Model Based RL, M.Sc. Thesis
2025 Sustainable City Management through Transformer-based RL with adapotive and boostrapped models, M.Sc. Thesis
2025 Playing Reconnaissance Blind Chess with AlphaZero, M.Sc. Thesis
2025 Symbolic Input Representations for DRL playing Atari, B.Sc. Thesis
2025 Attention-based Reward Shaping in RL via Transformers, M.Sc. Thesis
2024 LSTM-based beampath optimizer for the S-DALINAC, B.Sc. Thesis
2024 ISMCTS - Practical Enhancements using Transformers and Theoretical Guarantees for Best Responses, M.Sc. Thesis
2024 Game Phase Specific Models in A0, M.Sc. Thesis
2023 Transformers for Urban Energy Management, B.Sc. Thesis
2023 Using Graph Neural Networks to Improve Generalization in Self-Play Reinforcement Learning, M.Sc. Thesis
2022 Replacing PUCT with a Planning Model, M.Sc. Thesis
2022 Multimodal Learning for Chess, B.Sc. Thesis
2022 Creating an Agent for the Chess Variant Reconnaissance Blind Chess (RBC), M.Sc. Thesis

Supervised Courses and Projects.
WiSe 2025/26 JaxTari Lab, Practical Lab, with Quentin Delfosse, Raban Emund, Paul Seitz and Sebastian Wette
WiSe 2025/26 Einführung in die Künstliche Intelligenz, with Prof. Dr. Kristian Kersting, Cedric Derstroff
SoSe 2025 JaxTari Lab, Practical Lab, with Quentin Delfosse
WiSe 2024/25 Arcade 2.0, B.Sc.-Praktikum, with Quentin Delfosse
WiSe 2024/25 Object-centric Reinforcement Learning Lab, Practical Lab, with Quentin Delfosse
WiSe 2024/25 Einführung in die Künstliche Intelligenz, with Prof. Dr. Kristian Kersting, Johannes Czech
WiSe 2023/24 Einführung in die Künstliche Intelligenz, with Prof. Dr. Kristian Kersting, Johannes Czech, Dr. Martin Mundt,
WiSe 2023/24 HackAtari - Object manipulation using the RAM representation in Atari 2600, B.Sc.-Praktikum, with Quentin Delfosse
WiSe 2022/23 Einführung in die Künstliche Intelligenz, with Prof. Dr. Kristian Kersting, Johannes Czech, Dr. Martin Mundt,
WiSe 2022/23 AtariARI 2.0 - Object extraction using the RAM representation in Atari 2600, B.Sc.-Praktikum, with Quentin Delfosse


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