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arXiv · 2004.13242

Efficient Black-Box Planning Using Macro-Actions with Focused Effects

Cameron Allen·Michael Katz·Tim Klinger·George Konidaris·Matthew Riemer·Gerald Tesauro
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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.

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Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro. 2020-04-28. Efficient Black-Box Planning Using Macro-Actions with Focused Effects. https://arxiv.org/abs/2004.13242

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