Finding Options that Minimize Planning Time
Artificial Intelligence
2019-03-19 v3
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 including the classic four-rooms problem.
Cite
@article{arxiv.1810.07311,
title = {Finding Options that Minimize Planning Time},
author = {Yuu Jinnai and David Abel and D Ellis Hershkowitz and Michael Littman and George Konidaris},
journal= {arXiv preprint arXiv:1810.07311},
year = {2019}
}