The Logical Options Framework
Abstract
Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF’s learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps on our benchmarks. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.
Cite
Text
Araki et al. "The Logical Options Framework." International Conference on Machine Learning, 2021.Markdown
[Araki et al. "The Logical Options Framework." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/araki2021icml-logical/)BibTeX
@inproceedings{araki2021icml-logical,
title = {{The Logical Options Framework}},
author = {Araki, Brandon and Li, Xiao and Vodrahalli, Kiran and Decastro, Jonathan and Fry, Micah and Rus, Daniela},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {307-317},
volume = {139},
url = {https://mlanthology.org/icml/2021/araki2021icml-logical/}
}