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Philip Becker-Ehmck
Philip Becker-Ehmck
Volkswagen Group
Verified email at volkswagen.de
Title
Cited by
Cited by
Year
Switching Linear Dynamics for Variational Bayes Filtering
P Becker-Ehmck, J Peters, P van der Smagt
36th International Conference on Machine Learning (ICML), 2018
632018
Unsupervised real-time control through variational empowerment
M Karl, P Becker-Ehmck, M Soelch, D Benbouzid, P van der Smagt, ...
The International Symposium of Robotics Research, 158-173, 2019
602019
Learning to Fly via Deep Model-Based Reinforcement Learning
P Becker-Ehmck, M Karl, J Peters, P van der Smagt
arXiv preprint arXiv:2003.08876, 2020
472020
Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress
P Becker-Ehmck, M Karl, J Peters, P van der Smagt
ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021
62021
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
N Das, M Karl, P Becker-Ehmck, P van der Smagt
arXiv preprint arXiv:1911.00756, 2019
52019
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
X Zhang, P Becker-Ehmck, P van der Smagt, M Karl
Thirty-Seventh Conference on Advances in Neural Information Processing Systems, 2023
42023
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
M Alles, P Becker-Ehmck, P van der Smagt, M Karl
The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024
2024
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models
X Zhang, P Becker-Ehmck, P van der Smagt, M Karl
arXiv preprint arXiv:2404.18896, 2024
2024
Latent State-Space Models for Control
P Becker-Ehmck
Technische Universität Darmstadt, 2022
2022
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