Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications, often being the fundamental step in multi-agent systems. The increasing complexity of MAPF in complex and crowded environments, however, critically diminishes the effectiveness of existing solutions. In contrast to other studies that have either presented a general overview of the recent advancements in MAPF or extensively reviewed Deep Reinforcement Learning (DRL) within multi-agent system settings independently, our work presented in this review paper focuses on highlighting the integration of DRL-based approaches in MAPF. Moreover, we aim to bridge the current gap in evaluating MAPF solutions by addressing the lack of unified evaluation metrics and providing comprehensive clarification on these metrics. Finally, our paper discusses the potential of model-based DRL as a promising future direction and provides its required foundational understanding to address current challenges in MAPF. Our objective is to assist readers in gaining insight into the current research direction, providing unified metrics for comparing different MAPF algorithms and expanding their knowledge of model-based DRL to address the existing challenges in MAPF.

翻译：多智能体路径规划（MAPF）是许多大规模机器人应用中的关键领域，通常构成多智能体系统的基础步骤。然而，在复杂且拥挤环境中日益增长的MAPF复杂性，严重削弱了现有解决方案的有效性。与那些或对MAPF近期进展进行总体概述，或独立地在多智能体系统背景下广泛综述深度强化学习（DRL）的研究不同，本综述论文的工作聚焦于强调基于DRL的方法在MAPF中的融合。此外，我们旨在通过弥补评估指标统一的缺失，并对这些指标提供详尽的澄清，来填补当前MAPF解决方案评估方面的空白。最后，本文讨论了基于模型的DRL作为未来有前途方向的潜力，并提供了应对当前MAPF挑战所需的基础理解。我们的目标是帮助读者洞察当前研究趋势，提供统一指标以比较不同MAPF算法，并拓展其基于模型DRL的知识，以应对MAPF中存在的挑战。