Finn Rietz
Finn Rietz Position: Doctoral Student School/office: School of Science and TechnologyEmail: Zmlubi5yaWV0ejtvcnUuc2U=
Phone: No number available
Room: T1224
About Finn Rietz
I am a Ph.D. student at the Autonomous Mobile Manipulation Lab, originally from Hamburg, Germany.
My research interests are, broadly speaking, Deep Reinforcement Learning (DRL), Explainable AI (XAI), and Robotics. I believe that the DRL framework has immense potential for industry automation and optimization, but also think that the intransparency of Deep Neural Network-based AI systems must be addressed for safe real-world employment of these technologies. This is the overall problem I hope to contribute towards within the scope of my Ph.D.
Research groups
Publications
Articles in journals
- Rietz, F. , Magg, S. , Heintz, F. , Stoyanov, T. , Wermter, S. & Stork, J. A. (2023). Hierarchical goals contextualize local reward decomposition explanations. Neural Computing & Applications, 35 (23), 16693-16704. [BibTeX]
Conference papers
- Rietz, F. , Schaffernicht, E. , Heinrich, S. & Stork, J. A. (2024). Prioritized soft q-decomposition for lexicographic reinforcement learning. In: 12th International Conference on Learning Representations, ICLR 2024. Paper presented at 12th International Conference on Learning Representations, ICLR 2024, Vienna, May 7-11, 2024. International Conference on Learning Representations, ICLR. [BibTeX]
- Rietz, F. & Stork, J. A. (2023). Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer. Paper presented at 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), Detroit, MI, USA, October 1-5, 2023. [BibTeX]
- Rietz, F. , Schaffernicht, E. , Stoyanov, T. & Stork, J. A. (2022). Towards Task-Prioritized Policy Composition. Paper presented at 35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 24-26, 2022. [BibTeX]