Vlearn: Off-Policy Learning with Efficient State-Value-Function Estimation
Fabian Otto, Philipp Becker, Vien Anh Ngo, Gerhard NeumannUnder Submission
2023
Under Submission
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Ge Li, Hongyi Zhou, Dominik Roth, Serges Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard NeumannUnder Submission
Reinforcement Learning from Multiple Sensors via Joint Representations
Philipp Becker, Sebastian Markgraf, Fabian Otto, Gerhard NeumannarXiv preprint
MP3: Movement Primitive-Based (Re-)Planning Policy
Fabian Otto, Hongyi Zhou, Onur Celik, Ge Li, Rudolf Lioutikov, Gerhard NeumannCoRL Workshop on Learning Effective Abstractions for Planning (LEAP)
ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives
Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard NeumannIEEE Robotics and Automation Letters (RA-L)
International Conference on Intelligent Robots and Systems (IROS)
2022
Deep Black-Box Reinforcement Learning with Movement Primitives
Fabian Otto, Onur Celik, Hongyi Zhou, Hanna Ziesche, Ngo Anh Vien, Gerhard NeumannConference on Robot Learning (CoRL)
2021
Differentiable Trust Region Layers for Deep Reinforcement Learning
Fabian Otto, Philipp Becker, Ngo Anh Vien, Hanna Carolin Ziesche, Gerhard NeumannInternational Conference for Learning Representations (ICLR)
2020
Model-free Deep Reinforcement Learning – Algorithms and Applications
Fabian OttoReinforcement Learning Algorithms: Analysis and Applications. Studies in Computational Intelligence, vol 883.