Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines for when to use which algorithm. In this work, we develop the deep convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor), which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms. We improve the performance of our model by including single-agent shortest paths in the instance embedding given to our model and by utilizing supplemental loss functions in addition to a classification loss. We evaluate our model on a large and diverse dataset of MAPF instances, showing that it outperforms all individual algorithms in its portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We also provide an analysis of algorithm behavior in our dataset to gain a deeper understanding of optimal MAPF algorithms' strengths and weaknesses to help other researchers leverage different heuristics in algorithm designs.

中文翻译：

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MAPFAST：使用最短路径嵌入进行多代理路径查找的深度算法选择器

对于使跨度和总到达时间最小化而言，最佳地解决多代理商路径发现（MAPF）问题被称为NP-Hard。尽管已经开发了许多算法来解决MAPF问题，但尚没有一种能在所有类型的问题中都能很好地发挥作用的最佳MAPF算法，也没有何时使用哪种算法的标准指南。在这项工作中，我们开发了深度卷积网络MAPFAST（多代理路径查找算法SelecTor），该网络采用MAPF问题实例，并尝试从算法组合中选择使用最快的算法。通过在给模型提供的实例嵌入中包括单主体最短路径，并利用分类损失之外的补充损失函数，我们改善了模型的性能。我们在庞大且多样化的MAPF实例数据集上评估了我们的模型，表明该模型优于其产品组合中的所有单独算法以及最新的最佳MAPF算法选择器。我们还提供了数据集中算法行为的分析，以更深入地了解最佳MAPF算法的优缺点，以帮助其他研究人员在算法设计中利用不同的启发式方法。