On the Complexity of Solving Markov Decision Problems
Artificial Intelligence
2013-02-21 v1
Abstract
Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI researchers studying automated planning and reinforcement learning. In this paper, we summarize results regarding the complexity of solving MDPs and the running time of MDP solution algorithms. We argue that, although MDPs can be solved efficiently in theory, more study is needed to reveal practical algorithms for solving large problems quickly. To encourage future research, we sketch some alternative methods of analysis that rely on the structure of MDPs.
Cite
@article{arxiv.1302.4971,
title = {On the Complexity of Solving Markov Decision Problems},
author = {Michael L. Littman and Thomas L. Dean and Leslie Pack Kaelbling},
journal= {arXiv preprint arXiv:1302.4971},
year = {2013}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)