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.

翻译：最优求解多智能体路径规划（MAPF）问题在最小化完成时间与总到达时间两方面均被证明是NP难问题。尽管已有许多算法被开发用于求解MAPF问题，但目前既不存在适用于所有问题类型的优势最优MAPF算法，也缺乏关于何时应采用何种算法的标准指导原则。本研究中，我们开发了深度卷积网络MAPFAST（多智能体路径规划算法选择器），该网络接收MAPF问题实例并尝试从算法组合中选择最快的求解算法。我们通过以下方式提升模型性能：在输入模型的实例嵌入中包含单智能体最短路径信息，以及在分类损失函数之外引入辅助损失函数。我们在大规模且多样化的MAPF实例数据集上评估模型性能，结果表明其表现优于算法组合中的所有独立算法以及当前最先进的最优MAPF算法选择器。此外，我们通过分析数据集中算法的行为特征，深入理解各最优MAPF算法的优势与局限，以助力其他研究者在算法设计中有效利用不同的启发式策略。