Solving complex planning problems has been a long-standing challenge in
computer science. Learning-based subgoal search methods have shown promise in
tackling these problems, but they often suffer from a lack of completeness
guarantees, meaning that they may fail to find a solution even if one exists.
In this paper, we propose an efficient approach to augment a subgoal search
method to achieve completeness in discrete action spaces. Specifically, we
augment the high-level search with low-level actions to execute a multi-level
(hybrid) search, which we call complete subgoal search. This solution achieves
the best of both worlds: the practical efficiency of high-level search and the
completeness of low-level search. We apply the proposed search method to a
recently proposed subgoal search algorithm and evaluate the algorithm trained
on offline data on complex planning problems. We demonstrate that our complete
subgoal search not only guarantees completeness but can even improve
performance in terms of search expansions for instances that the high-level
could solve without low-level augmentations. Our approach makes it possible to
apply subgoal-level planning for systems where completeness is a critical
requirement.

提出了一种有效的方法来增加子目标搜索的完备性，在复杂规划问题中应用子目标级别规划，提高性能并保证完备性。