Tim Hertweck
Tim Hertweck
Google DeepMind
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Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
D Schwab, T Springenberg, MF Martins, T Lampe, M Neunert, ...
arXiv preprint arXiv:1902.04706, 2019
Regularized hierarchical policies for compositional transfer in robotics
M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ...
Compositional Transfer in Hierarchical Reinforcement Learning
M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ...
Disentangled cumulants help successor representations transfer to new tasks
C Grimm, I Higgins, A Barreto, D Teplyashin, M Wulfmeier, T Hertweck, ...
arXiv preprint arXiv:1911.10866, 2019
Data-efficient hindsight off-policy option learning
M Wulfmeier, D Rao, R Hafner, T Lampe, A Abdolmaleki, T Hertweck, ...
International Conference on Machine Learning, 11340-11350, 2021
Towards general and autonomous learning of core skills: A case study in locomotion
R Hafner, T Hertweck, P Klöppner, M Bloesch, M Neunert, M Wulfmeier, ...
arXiv preprint arXiv:2008.12228, 2020
Representation matters: improving perception and exploration for robotics
M Wulfmeier, A Byravan, T Hertweck, I Higgins, A Gupta, T Kulkarni, ...
2021 IEEE International Conference on Robotics and Automation (ICRA), 6512-6519, 2021
Is curiosity all you need? on the utility of emergent behaviours from curious exploration
O Groth, M Wulfmeier, G Vezzani, V Dasagi, T Hertweck, R Hafner, ...
arXiv preprint arXiv:2109.08603, 2021
Simple sensor intentions for exploration
T Hertweck, M Riedmiller, M Bloesch, JT Springenberg, N Siegel, ...
arXiv preprint arXiv:2005.07541, 2020
The Challenges of Exploration for Offline Reinforcement Learning
N Lambert, M Wulfmeier, W Whitney, A Byravan, M Bloesch, V Dasagi, ...
arXiv preprint arXiv:2201.11861, 2022
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