Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal

• URL: http://arxiv.org/abs/2310.19463v1
• Date: Mon, 30 Oct 2023 11:39:49 GMT
• Title: Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
• Authors: Leah Chrestien, Tom\'as Pevn\'y, Stefan Edelkamp, Anton\'in Komenda
• Abstract summary: In imitation learning for planning, parameters of functions are optimized against a set of solved problem instances. It then proposes a family of loss functions based on ranking tailored for a given variant of the forward search algorithm. The experimental comparison on a diverse set of problems unequivocally supports the derived theory.
• Score: 0.9217021281095907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient conditions of strictly optimally efficient heuristics for forward search algorithms, mainly A* and greedy best-first search, which expand only states on the returned optimal path. It then proposes a family of loss functions based on ranking tailored for a given variant of the forward search algorithm. Furthermore, from a learning theory point of view, it discusses why optimizing cost-to-goal \hstar\ is unnecessarily difficult. The experimental comparison on a diverse set of problems unequivocally supports the derived theory.

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