Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance. However, such hierarchical structures cannot be exploited by current policy search algorithms. We will concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network fi rst decides which of the options to execute and, subsequently, the option-policy determines the action. In this paper, we reformulate learning a hierarchical policy as a latent variable estimation problem and subsequently extend the Relative Entropy Policy Search (REPS) to the latent variable case. We show that our Hierarchical REPS can learn versatile solutions while also showing an increased performance in terms of learning speed and quality of the found policy in comparison to the nonhierarchical approach.
| Author(s): | Daniel, C. and Neumann, G. and Peters, J. |
| Links: | |
| Book Title: | Fifteenth International Conference on Artificial Intelligence and Statistics |
| Volume: | 22 |
| Pages: | 273--281 |
| Year: | 2012 |
| Month: | April |
| Day: | 0 |
| Series: | JMLR Proceedings |
| Editors: | Lawrence, N. D. and Girolami, M. |
| Publisher: | JMLR.org |
| Bibtex Type: | Conference Paper (inproceedings) |
| Event Name: | AISTATS 2012 |
| Event Place: | La Palma, Canary Islands, Spain |
| Electronic Archiving: | grant_archive |
BibTex
@inproceedings{DanielNP2012,
title = {Hierarchical Relative Entropy Policy Search},
booktitle = {Fifteenth International Conference on Artificial Intelligence and Statistics},
abstract = {Many real-world problems are inherently hierarchically
structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance. However, such hierarchical structures cannot be exploited by current policy search algorithms. We will concentrate on a basic, but highly relevant hierarchy - the
`mixed option' policy. Here, a gating network fi rst decides which of the options to execute and, subsequently, the option-policy determines the action. In this paper, we reformulate learning a hierarchical policy as a latent variable estimation problem and subsequently extend the
Relative Entropy Policy Search (REPS) to the latent variable case. We show that our Hierarchical REPS can learn versatile solutions while also showing an increased performance in terms of learning speed and quality
of the found policy in comparison to the nonhierarchical
approach.},
volume = {22},
pages = {273--281},
series = {JMLR Proceedings},
editors = {Lawrence, N. D. and Girolami, M.},
publisher = {JMLR.org},
month = apr,
year = {2012},
slug = {danielnp2012},
author = {Daniel, C. and Neumann, G. and Peters, J.},
month_numeric = {4}
}