标题： SLOPE：基于学习最优剪枝扩展的搜索 显示英文标题

标题： SLOPE: Search with Learned Optimal Pruning-based Expansion

摘要： 启发式搜索常用于运动规划和路径查找问题，以在图中找到最短路径，同时保证完整性和最优效率。 缺点是其空间复杂度，特别是需要在内存中存储所有扩展的子节点，并对大量活动节点进行排序，在实时场景中有限的板载计算能力下可能会成为问题。 为了解决这个问题，我们提出了基于学习最优剪枝的扩展搜索（SLOPE），它学习节点到可能最优路径的距离，与其他方法学习剩余成本值不同。 然后根据所述距离剪除不受欢迎的节点，从而减少开放列表的大小。 这确保了搜索仅探索接近最优路径的区域，同时降低内存和计算成本。 与传统的学习方法不同，我们的方法与估计剩余成本启发式的方法正交，为提高搜索效率提供了一种补充策略。 我们通过将其作为独立的搜索方法以及与学习到的启发式函数结合来评估其有效性，实现了可比或更好的节点扩展指标，同时减少了开放列表中的子节点数量。 我们的代码可在 https://github.com/dbokan1/SLOPE 获取。 显示英文摘要

摘要： Heuristic search is often used for motion planning and pathfinding problems, for finding the shortest path in a graph while also promising completeness and optimal efficiency. The drawback is it's space complexity, specifically storing all expanded child nodes in memory and sorting large lists of active nodes, which can be a problem in real-time scenarios with limited on-board computation. To combat this, we present the Search with Learned Optimal Pruning-based Expansion (SLOPE), which, learns the distance of a node from a possible optimal path, unlike other approaches that learn a cost-to-go value. The unfavored nodes are then pruned according to the said distance, which in turn reduces the size of the open list. This ensures that the search explores only the region close to optimal paths while lowering memory and computational costs. Unlike traditional learning methods, our approach is orthogonal to estimating cost-to-go heuristics, offering a complementary strategy for improving search efficiency. We demonstrate the effectiveness of our approach evaluating it as a standalone search method and in conjunction with learned heuristic functions, achieving comparable-or-better node expansion metrics, while lowering the number of child nodes in the open list. Our code is available at https://github.com/dbokan1/SLOPE.