Finding Options That Minimize Planning Time
Abstract
We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maximum of value-iteration passes. We first show that the problem is $\NP$-hard, even if the task is constrained to be deterministic—the first such complexity result for option discovery. We then present the first polynomial-time boundedly suboptimal approximation algorithm for this setting, and empirically evaluate it against both the optimal options and a representative collection of heuristic approaches in simple grid-based domains.
Cite
Text
Jinnai et al. "Finding Options That Minimize Planning Time." International Conference on Machine Learning, 2019.Markdown
[Jinnai et al. "Finding Options That Minimize Planning Time." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/jinnai2019icml-finding/)BibTeX
@inproceedings{jinnai2019icml-finding,
title = {{Finding Options That Minimize Planning Time}},
author = {Jinnai, Yuu and Abel, David and Hershkowitz, David and Littman, Michael and Konidaris, George},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {3120-3129},
volume = {97},
url = {https://mlanthology.org/icml/2019/jinnai2019icml-finding/}
}