@inproceedings{5177,
  title = {Policy Learning: A Unified Perspective with Applications in Robotics},
  journal = {Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008)},
  booktitle = {EWRL 2008},
  abstract = {Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning algorithms from a common point of view, i.e, policy gradient algorithms, natural-gradient algorithms and EM-like policy learning. Secondly, we present several applications to both robot motor primitive learning as well as to robot control in task space. Results both from simulation and several different real robots are shown.},
  pages = {220-228},
  editors = {Girgin, S. , M. Loth, R. Munos, P. Preux, D. Ryabko},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = nov,
  year = {2008},
  slug = {5177},
  author = {Peters, J. and Kober, J. and Nguyen-Tuong, D.},
  month_numeric = {11}
}