Publications by Venue

• ICLR 2
• CoRL 2
• Book Chapter 1
• RA-L 1
• IROS 1
• RLC 1
• ArXiv 1

ICLR

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 Neumann
International Conference for Learning Representations (ICLR)

Differentiable Trust Region Layers for Deep Reinforcement Learning

Fabian Otto, Philipp Becker, Ngo Anh Vien, Hanna Carolin Ziesche, Gerhard Neumann
International Conference for Learning Representations (ICLR)

CoRL

MP3: Movement Primitive-Based (Re-)Planning Policy

Fabian Otto, Hongyi Zhou, Onur Celik, Ge Li, Rudolf Lioutikov, Gerhard Neumann
CoRL Workshop on Learning Effective Abstractions for Planning (LEAP)

Deep Black-Box Reinforcement Learning with Movement Primitives

Fabian Otto, Onur Celik, Hongyi Zhou, Hanna Ziesche, Ngo Anh Vien, Gerhard Neumann
Conference on Robot Learning (CoRL)

Book Chapter

Model-free Deep Reinforcement Learning – Algorithms and Applications

Fabian Otto
Reinforcement Learning Algorithms: Analysis and Applications. Studies in Computational Intelligence, vol 883.

RA-L

ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives

Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann
IEEE Robotics and Automation Letters (RA-L)
International Conference on Intelligent Robots and Systems (IROS)

IROS

ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives

Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann
IEEE Robotics and Automation Letters (RA-L)
International Conference on Intelligent Robots and Systems (IROS)

RLC

Reinforcement Learning from Multiple Sensors via Joint Representations

Philipp Becker, Sebastian Mossburger, Fabian Otto, Gerhard Neumann
Reinforcement Learning Conference (RLC)

ArXiv

Vlearn: Off-Policy Learning with Efficient State-Value-Function Estimation

Fabian Otto, Philipp Becker, Vien Anh Ngo, Gerhard Neumann
Under Submission

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