DREAM: Distributed Regional Efficient Agent Management with LLMs for Online Multi-Agent Pathfinding

In this paper, we introduce DREAM, Distributed Regional Efficient Agent Management, a novel method using Large Language Models (LLMs) to solve Multi-Agent Pathfinding (MAPF) problems in complicated environments. Our approach splits up the area into various local regions and an LLM agent handles each one of them intelligently in reasoning and decision making. We present some novel designs in our system: 1) Adaptive region management and allocation to regions, supporting the dynamic partitioning of different complexity or density areas. 2) The multi-level LLM-driven agents collaboration framework that enables peer-peer, interLLM coordination and controls for effective monitoring intelligence across a hierarchical path planning organization hierarchy level ensures autonomy whilst improving overall understanding among LLM agents, leading to more accurate planning decisions from real-time analysis. (3) Failurereflection- replanning mechanism integrated within an individual LLM's management scope eventually results continual improvement. (4) LLM agents can do function calling to interact with the typical algorithms also. Our system successfully processes complex and large-scale MAPF scenarios by merging the higher-orderality of reasoning capabilities in LLMs with this novel distributed framework. For instance, the distributed and hierarchical nature of this approach helps to break a high-dimensional MAPF problem into several groups of smaller dimension. As such, this approach also opens up the development of AI language models in more complex robotics and logistics scenarios, potentially changing how multi-agent coordination is done for actual situations.