Hierarchical Skill Learning for High-Level Planning
Abstract
I present skill bootstrapping, a proposed new research direction for agent learning and planning that allows an agent to start with low-level primitive actions, and develop skills that can be used for higher-level planning. Skills are developed over the course of solving many different problems in a domain, using reinforcement learning techniques to complement the benefits and disadvantages of heuristic-search planning. I describe the overall architecture of the proposed approach and discuss how it relates to other work.
Cite
Text
MacGlashan. "Hierarchical Skill Learning for High-Level Planning." AAAI Conference on Artificial Intelligence, 2010.Markdown
[MacGlashan. "Hierarchical Skill Learning for High-Level Planning." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/macglashan2010aaai-hierarchical/)BibTeX
@inproceedings{macglashan2010aaai-hierarchical,
title = {{Hierarchical Skill Learning for High-Level Planning}},
author = {MacGlashan, James},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2010},
url = {https://mlanthology.org/aaai/2010/macglashan2010aaai-hierarchical/}
}