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This review paper provides an in-depth analysis of the latest advancements in applying Machine Learning (ML) to solve the Multi-Agent Path Finding (MAPF) problem. The MAPF problem is about finding collision-free paths for multiple agents to travel from their source to goal locations in a known environment. This method underpins a range of advanced, large-scale automated systems, notably in warehouse logistics. The existing research on conventional MAPF is extensive; however, recent developments in ML have notably augmented the capabilities of MAPF techniques. This research seeks to thoroughly investigate the emerging field focused on using ML to help solve the MAPF problem. It aims to highlight the transformative potential of ML in enhancing the efficiency and effectiveness of multi-agent systems in navigating and coordinating in complex environments. Our study comprehensively examines the entire MAPF process, encompassing environment representation, path planning, and solution execution.
Alkazzi et al. (Mon,) studied this question.
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