- Reinforcement Learning
- Model-based Reinforcement Learning
- Machine Learning
- TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department
- Hessian Centre for Artificial Intelligence
NeurIPS, ICLR, CoRL, and various Robotics & ML workshops.
|Computational Engineering and Robotics||SS 2022, WS 2022/23|
|Statistical Machine Learning||SS 2023, WS 2023/24|
Daniel joined the Intelligent Autonomous System lab on October 1st, 2021 as a Ph.D. student. He is part of the 3AI project with Hessian.AI. In his research, Daniel focuses on increasing sample efficiency of model-based reinforcement learning algorithms by studying the impact which model-errors have on the learning.
Before starting his Ph.D., Daniel completed his Bachelor's degree and Master's degree in Wirtschaftsinformatik at the Technische Universität Darmstadt. He wrote his Master's thesis entitled "Dyna-Style Model-Based Reinforcement Learning with Value Expansion" in the Computer Science Department under the supervision of Michael Lutter and Jan Peters. During his studies, Daniel further did two research internships, at the Bosch Center for AI and at Huawei Noah’s Ark Lab London.
- , arXiv preprint.
- , International Conference on Learning Representations (ICLR).
- , Advances in Neural Information Processing Systems (NIPS / NeurIPS).
- , Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
- , Machine Learning.
- , Reinforcement Learning Algorithms: Analysis and Applications, pp.49-57.
- , Studies in Computational Intelligence, Springer International Publishing.
- Zhou, M.; Luo, J.; Villella, J.; Yang, Y.; Rusu, D.; Miao, J.; Zhang, W.; Alban, M.; Fadakar, I.; Chen, Z.; Chongxi-Huang, A.; Wen, Y.; Hassanzadeh, K.; Graves, D.; Chen, D.; Zhu, Z.; Nguyen, N.; Elsayed, M.; Shao, K.; Ahilan, S.; Zhang, B.; Wu, J.; Fu, Z.; Rezaee, K.; Yadmellat, P.; Rohani, M.; Perez-Nieves, N.; Ni, Y.; Banijamali, S.; Cowen-Rivers, A.; Tian, Z.; Palenicek, D.; Bou-Ammar, H.; Zhang, H.; Liu, W.; Hao, J.; Wang, J. (2020). SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving. Conference on Robot Learning (CoRL). Best System Paper Award. PDF
|RL:IP.WS23/24||Dennert D., Scherer C., Ahmad F.||XXX: eXploring X-Embodiment with RT-X||Tim Schneider & Theo Gruner & Maximilian Tölle|
|RL:IP.WS23/24||Morton C., Jacobs T.||XXX: eXploring X-Embodiment with RT-X||Tim Schneider & Theo Gruner & Maximilian Tölle|
|RL:IP.WS23/24||Böhm A., Pfenning I., Lenz J.||Unveiling the Unseen: Tactile Perception and Reinforcement Learning in the Real World||Tim Schneider & Theo Gruner|
|RL:IP.SS23||Krämer E.||Latent Tactile Representations for Model-based RL||Tim Schneider & Theo Gruner|