PC2P: Multi-Agent Path Finding via Personalized-Enhanced Communication and Crowd Perception

TL;DR

This paper introduces PC2P, a novel multi-agent pathfinding method that enhances communication and perception capabilities, leading to improved coordination and deadlock resolution in complex environments.

Contribution

The paper presents a personalized-enhanced communication mechanism and crowd perception integration within a MARL framework, addressing scalability and deadlock issues in distributed MAPF.

Findings

Outperforms state-of-the-art MAPF methods in diverse environments

Effective deadlock resolution through region-based strategies

Each module significantly improves overall performance

Abstract

Distributed Multi-Agent Path Finding (MAPF) integrated with Multi-Agent Reinforcement Learning (MARL) has emerged as a prominent research focus, enabling real-time cooperative decision-making in partially observable environments through inter-agent communication. However, due to insufficient collaborative and perceptual capabilities, existing methods are inadequate for scaling across diverse environmental conditions. To address these challenges, we propose PC2P, a novel distributed MAPF method derived from a Q-learning-based MARL framework. Initially, we introduce a personalized-enhanced communication mechanism based on dynamic graph topology, which ascertains the core aspects of ``who" and ``what" in interactive process through three-stage operations: selection, generation, and aggregation. Concurrently, we incorporate local crowd perception to enrich agents' heuristic observation, thereby strengthening the model's guidance for effective actions via the integration of static spatial constraints and dynamic occupancy changes. To resolve extreme deadlock issues, we propose a region-based deadlock-breaking strategy that leverages expert guidance to implement efficient coordination within confined areas. Experimental results demonstrate that PC2P achieves superior performance compared to state-of-the-art distributed MAPF methods in varied environments. Ablation studies further confirm the effectiveness of each module for overall performance.