  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.
