2008-07-09-toussaint08a.md

abstract	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Planning can often be simplified by decomposing the task into smaller tasks arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy discovery problem can be framed as a non-convex optimization problem. However, the inherent computational difficulty of solving such an optimization problem makes it hard to scale to real-world problems. In another line of research, Toussaint et al. [18] developed a method to solve planning problems by maximum-likelihood estimation. In this paper, we show how the hierarchy discovery problem in partially observable domains can be tackled using a similar maximum likelihood approach. Our technique first transforms the problem into a dynamic Bayesian network through which a hierarchical structure can naturally be discovered while optimizing the policy. Experimental results demonstrate that this approach scales better than previous techniques based on non-convex optimization.	Hierarchical POMDP controller optimization by likelihood maximization	2008	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	toussaint08a	0	Hierarchical POMDP controller optimization by likelihood maximization	562	570	562-570	562	false	Toussaint, Marc and Charlin, Laurent and Poupart, Pascal	given family Marc Toussaint given family Laurent Charlin given family Pascal Poupart	2008-07-09	Reissued by PMLR on 09 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	https://raw.githubusercontent.com/mlresearch/r6/main/assets/toussaint08a/toussaint08a.pdf