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. , Smirnov, O. , Karimi, S. & Gao, L. (2025). Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-task Offline RL. In: Yuqing Ma; Jinyang Guo; Xiaowei Zhao; Ruihao Gong; Ning Liu; Xuefei Ning; Xianglong Liu, Generalizing from Limited Resources in the Open World Third International Workshop, GLOW 2025, Held in Conjunction with IJCAI 2025, Montreal, Canada, August 16–22, 2025, Proceedings. Paper presented at 3rd International Workshop on Generalizing from Limited Resources in the Open World-GLOW, Montreal, Canada, August 16-22, 2025. (pp. 31-40). Springer. [BibTeX]
- 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]