Daniel Palenicek

Quick Info

Research Interests

Reinforcement Learning, Model-based Reinforcement Learning, Machine Learning, Robotics

More Information

CV Google Scholar GitHub

Contact Information

Daniel Palenicek
TU Darmstadt, FG IAS,
Hochschulstr. 10, 64289 Darmstadt
Office. Room E304, Building S2|02
work+49-6151-16-25387

Daniel joined the Intelligent Autonomous System lab on October, 1st, 2021 as a PhD student. # (:titlesearch palenicek)

Before starting his PhD, 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.

Research Interests

Reinforcement Learning, Model-based Reinforcement Learning, Machine Learning, Robotics

Key References

  1. Palenicek, D.; Lutter, M., Peters, J. (2022). Revisiting Model-based Value Expansion, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Cowen-Rivers, A.I.; Palenicek, D.; Moens, V.; Abdullah, M.A.; Sootla, A.; Wang, J.; Bou-Ammar, H. (2022). SAMBA: safe model-based & active reinforcement learning, Machine Learning.   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Palenicek, D. (2021). A Survey on Constraining Policy Updates Using the KL Divergence, Reinforcement Learning Algorithms: Analysis and Applications, pp.49-57.   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Belousov, B.; Abdulsamad H.; Klink, P.; Parisi, S.; Peters, J. (2021). Reinforcement Learning Algorithms: Analysis and Applications, Studies in Computational Intelligence, Springer International Publishing.   Download Article [PDF]   BibTeX Reference [BibTex]
  5. 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. Download Article

  

zum Seitenanfang