| Switching Linear Dynamics for Variational Bayes Filtering P Becker-Ehmck, J Peters, P van der Smagt 36th International Conference on Machine Learning (ICML), 2018 | 58 | 2018 |
| 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 | 56 | 2019 |
| 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 | 38 | 2020 |
| 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 | 5 | 2021 |
| 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 | 5 | 2019 |
| 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 |
| 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 | | 2023 |
| Latent State-Space Models for Control P Becker-Ehmck Technische Universität Darmstadt, 2022 | | 2022 |
Patrick van der SmagtVolkswagen AG; ELTE UniversityZweryfikowany adres z tum.de
