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Zehn ausgewählte Publikationen

• 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