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Sebastian Buck, Freek Stulp, Michael Beetz, and Thorsten Schmitt. Machine Control Using Radial Basis Value Functions and Inverse State Projection. In Proceedings of the IEEE Intl. Conference on Automation, Robotics, Control, and Vision, 2002, Singapore, 2002.
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Abstract
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.
BibTeX Entry
@InProceedings{buck02machine,
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 = {Proceedings of the IEEE Intl. Conference on Automation, Robotics, Control, and Vision, 2002, Singapore},
year = {2002},
pdf = {http://www9.in.tum.de/publications/2002/ICARCV-2002-Buck-etal.pdf},
url = {http://citeseer.nj.nec.com/buck02machine.html},
bib2html_pubtype = {Refereed Conference Paper},
bib2html_rescat = {Robotics - Motor Control},
abstract = {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.}
}
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