Main Content
Ten selected Publications
- Peters, J., Schaal, S. (2008).
Natural Actor-Critic
Neurocomputing, 71(7), pp.1180-90
DOI: 10.1016/j.neucom.2007.11.026 - Peters, J., Schaal, S. (2008).
Reinforcement Learning of Motor Skills with Policy Gradients
Neural Networks, 21(4), pp.682-97
DOI: 10.1016/j.neunet.2008.02.003 - Kober, J., Peters, J. (2011).
Policy Search for Motor Primitives in Robotics
Machine Learning, 84(1), pp.171–203
DOI: 10.1007/s10994-010-5223-6 - Mülling, K., Kober, J., Krömer, O., Peters, J. (2013).
Learning to Select and Gener- alize Striking Movements in Robot Table Tennis
International Journal of Robotics Re- search, 32(3), pp. 280–298
DOI: 10.1177/0278364912472380 - Daniel, C., Neumann, G., Kroemer, O., Peters, J. (2016).
Hierarchical Relative Entropy Policy Search
Journal of Machine Learning (JMLR), 17, pp.1–50 - Maeda, G., Ewerton, M., Neumann, G., Lioutikov, R., Peters, J. (2017).
Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration
International Journal of Robotics Research (IJRR)
- Lutter, M., Ritter, C., Peters, J. (2019).
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
International Conference on Learning Representations (ICLR) - D`Eramo, C., Tateo, D., Bonarini, A., Restelli, M., Peters, J. (2020).
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
International Conference in Learning Representations (ICLR) - Watson, J., Lin J. A., Klink, P., Pajarinen, J., Peters, J. (2021).
Latent Derivative Bayesian Last Layer Networks
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) - Akrour, R., Tateo, D., Peters, J. (2022).
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.6795-6806