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，常常是多智能体系统的基础步骤。然而，复杂和拥挤环境中MAPF复杂性的增加严重削弱了现有解决方案的有效性。与其他研究要么对MAPF的最新进展进行一般性概述，要么独立地在多智能体系统设置中广泛回顾深度强化学习（DRL）不同，本综述论文的工作侧重于突出基于DRL的方法在MAPF中的集成。此外，我们旨在通过解决缺乏统一评估指标的问题，并对这些指标提供全面澄清，来弥合当前评估MAPF解决方案的差距。最后，本文探讨了基于模型的DRL作为有前途的未来方向的潜力，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方向，提供用于比较不同MAPF算法的统一指标，并扩展他们对基于模型的DRL的知识，以应对MAPF中现有的挑战。