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.

翻译：解决复杂规划问题一直是计算机科学中的长期挑战。基于学习的子目标搜索方法在应对这些问题方面展现出潜力，但常因缺乏完备性保证而受限，即即使存在解也可能无法找到。本文提出一种高效方法，用于增强子目标搜索方法，使其在离散动作空间中实现完备性。具体而言，我们通过低层动作扩充高层搜索，以执行多层（混合）搜索，称之为完备子目标搜索。该方案兼顾两方面优势：高层搜索的实用效率与低层搜索的完备性。我们将所提搜索方法应用于近期提出的子目标搜索算法，并在离线数据训练的模型上评估其在复杂规划问题上的表现。实验表明，完备子目标搜索不仅保证完备性，甚至能改善高层搜索原本无需低层增强即可解决的实例中的搜索扩展性能。本方法使得子目标层级的规划能够应用于需满足完备性关键要求的系统。