Title: Reinforcement learning with the use of costly features
Authors: Goetschalckx, Robby
Sanner, Scott
Driessens, Kurt #
Issue Date: 2008
Publisher: IOS Press
Host Document: Proceedings of the 18th European Conference on Artificial Intelligence pages:779-780
Conference: ECAI edition:18 location:Patras, Greece date:21-25 July 2008
Article number: ML450
Abstract: A common solution approach to reinforcement learning problems with large state spaces (where value functions cannot be represented exactly) is to compute an approximation of the value function in terms of state features. However, little attention has been paid to the cost of computing these state features (e.g., search-based features). To this end, we introduce a cost-sensitive sparse linear-value function approximation algorithm --- FOVEA --- and demonstrate its performance on an experimental domain with a range of feature costs.
ISBN: 978-1-58603-891-5
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Informatics Section
# (joint) last author

Files in This Item:
File Description Status SizeFormat
paper450.pdfExtended abstract Published 77KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.