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Policy Learning: A Unified Perspective With Applications In Robotics

2008

Poster

ei


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 al- gorithms 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.

Author(s): Peters, J. and Kober, J. and Nguyen-Tuong, D.
Journal: 8th European Workshop on Reinforcement Learning for Robotics (EWRL 2008)
Volume: 8
Pages: 10
Year: 2008
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@poster{6748,
  title = {Policy Learning: A Unified Perspective With Applications In Robotics},
  author = {Peters, J. and Kober, J. and Nguyen-Tuong, D.},
  journal = {8th European Workshop on Reinforcement Learning for Robotics (EWRL 2008)},
  volume = {8},
  pages = {10},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2008},
  month_numeric = {7}
}