RI | Publications | Barycentric Interpolator for Continuous Space and Time Reinforcement Learning

Text only version of this site

Barycentric Interpolator for Continuous Space and Time Reinforcement Learning
R. Munos and A. Moore
Neural Information Processing Systems, MIT Press, Vol. 11, December, 1998.

Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference

Download [Help]

Adobe portable document format (pdf) [170 KB]
Compressed postscript (ps.gz) [48 KB]

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

In order to find the optimal control of continuous state-space and time reinforcement learning (RL) problems, we approximate the value function (VF) with a particular class of functions called the barycentric interpolators. We establish sufficient conditions under which a RL algorithm converges to the optimal VF, even when we use approximate models of the state dynamics and the reinforcement functions.

Notes

Associated lab/group: Auton Lab
Associated project: Auton Project

Number of pages: 7

Text Reference

R. Munos and A. Moore, "Barycentric Interpolator for Continuous Space and Time Reinforcement Learning," Neural Information Processing Systems, MIT Press, Vol. 11, December, 1998.

BibTeX Reference

@inproceedings{Munos_1998_2094,
   author = "Remi Munos and Andrew Moore",
   title = "Barycentric Interpolator for Continuous Space and Time Reinforcement Learning",
   booktitle = "Neural Information Processing Systems",
   month = "December",
   year = "1998",
   volume = "11",
   publisher = "MIT Press"
}