Non-Parametric Approximate Dynamic Programming via the Kernel Method
Abstract
This paper presents a novel non-parametric approximate dynamic programming (ADP) algorithm that enjoys graceful, dimension-independent approximation and sample complexity guarantees. In particular, we establish both theoretically and computationally that our proposal can serve as a viable alternative to state-of-the-art parametric ADP algorithms, freeing the designer from carefully specifying an approximation architecture. We accomplish this by developing a kernel-based mathematical program for ADP. Via a computational study on a controlled queueing network, we show that our non-parametric procedure is competitive with parametric ADP approaches.
Cite
Text
Bhat et al. "Non-Parametric Approximate Dynamic Programming via the Kernel Method." Neural Information Processing Systems, 2012.Markdown
[Bhat et al. "Non-Parametric Approximate Dynamic Programming via the Kernel Method." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/bhat2012neurips-nonparametric/)BibTeX
@inproceedings{bhat2012neurips-nonparametric,
title = {{Non-Parametric Approximate Dynamic Programming via the Kernel Method}},
author = {Bhat, Nikhil and Farias, Vivek and Moallemi, Ciamac C.},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {386-394},
url = {https://mlanthology.org/neurips/2012/bhat2012neurips-nonparametric/}
}