Efficient Black-Box Planning Using Macro-Actions with Focused Effects
Abstract
The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without an explicit model of the domain. Heuristics can make search more efficient, but goal-aware heuristics for black-box planning usually rely on goal counting, which is often quite uninformative. In this work, we show how to overcome this limitation by discovering macro-actions that make the goal-count heuristic more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.
Keywords
Cite
@article{arxiv.2004.13242,
title = {Efficient Black-Box Planning Using Macro-Actions with Focused Effects},
author = {Cameron Allen and Michael Katz and Tim Klinger and George Konidaris and Matthew Riemer and Gerald Tesauro},
journal= {arXiv preprint arXiv:2004.13242},
year = {2021}
}
Comments
To appear at IJCAI 2021; code available at https://github.com/camall3n/focused-macros