@inproceedings{Buc02Mac,
  author    = {Sebastian Buck and Freek Stulp and Michael Beetz and Thorsten Schmitt},
  title     = {{Machine Control Using Radial Basis Value Functions and Inverse State Projection}},
  booktitle = {Proc. of the IEEE Intl. Conf. on Automation, Robotics, Control, and Vision},
  year      = {2002},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Models, Learning, Action},
  bib2html_groups   = {AGILO},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {Robot},
  abstract = {Typical real world machine control tasks have some characteristics
  which makes them difficult to solve: Their state spaces are
  high-dimensional and continuous, and it may be impossible to reach a
  satisfying target state by exploration or human control. To overcome
  these problems, in this paper, we propose (1) to use radial basis
  functions for value function approximation in continuous space
  reinforcement learning and (2) the use of learned inverse projection
  functions for state space exploration. We apply our approach to path
  planning in dynamic environments and to an aircraft autolanding
  simulation, and evaluate its performance.}
}