DMCA
Towards a Unified Theory of State Abstraction for MDPs (2006)
Cached
Download Links
| Venue: | In Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics |
| Citations: | 60 - 5 self |
BibTeX
@INPROCEEDINGS{Li06towardsa,
author = {Lihong Li and Thomas J. Walsh and Michael L. Littman},
title = {Towards a Unified Theory of State Abstraction for MDPs},
booktitle = {In Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics},
year = {2006},
pages = {531--539}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
extensively studied in the fields of artificial intelligence and operations research. Instead of working in the ground state space, the decision maker usually finds solutions in the abstract state space much faster by treating groups of states as a unit by ignoring irrelevant state information. A number of abstractions have been proposed and studied in the reinforcement-learning and planning literatures, and positive and negative results are known. We provide a unified treatment of state abstraction for Markov decision processes. We study five particular abstraction schemes, some of which have been proposed in the past in di#erent forms, and analyze their usability for planning and learning.
