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Gabriel Kalweit

gabriel.pngPhD Student

Institut für Informatik
Albert-Ludwigs-Universität
Freiburg Georges-Koehler-Allee 080, Room 00-022
79110 Freiburg im Breisgau

Tel.: +49 761 203 8022

Email: Email Gabriel

 

 

 

Preprints

  • Gabriel Kalweit*, Maria Huegle*, Moritz Werling and Joschka Boedecker. Interpretable Multi Time-scale Constraints in Model-free Deep Reinforcement Learning for Autonomous Driving. 2020. arxiv Video
  • Gabriel Kalweit, Maria Huegle, Joschka Boedecker. Off-policy Multi-step Q-learning. 2019. arxiv

 

Publications

 

2020

  • Maria Huegle, Gabriel Kalweit, Moritz Werling and Joschka Boedecker. Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving. Accepted at ICRA 2020. arxiv Video
  • Oier Mees*, Markus Merklinger*, Gabriel Kalweit and Wolfram Burgard. Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video. Accepted at ICRA 2020. arxiv  Project Page Dataset Video
  • Maria Hügle, Gabriel Kalweit, Thomas Hügle and Joschka Boedecker. A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis. AAAI 2020 Workshop on Health Intelligence. To appear in Studies in Computational Intelligence, Springer (2020).

 

2019

  • Maria Huegle*, Gabriel Kalweit*, Branka Mirchevska, Moritz Werling and Joschka Boedecker. Dynamic Input for Deep Reinforcement Learning in Autonomous Driving. Accepted at IROS 2019. arxiv  Project Page Video
  • Gabriel Kalweit, Maria Huegle and Joschka Boedecker. Composite Q-Learning. Combining Learning and Reasoning – Towards Human-Level Robot Intelligence. PDF
  • Markus Merklinger, Oier Mees, Gabriel Kalweit and Wolfram Burgard. Adversarial Skill Networks: Unsupervised Skill Learning from Video.

 

2017

  • Gabriel Kalweit, Joschka Boedecker (2017) Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning. Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:195-206. PDF  Video

 

Teaching

  • TA, WS2019/20: Reinforcement Learning (Lecture)
  • TA, WS2018/19: Reinforcement Learning (Lecture)
  • Co-organizer, SS2018: Hierarchical Reinforcement Learning (Seminar)
  • TA, WS2017/18: Reinforcement Learning (Lecture)