Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system. MAPF finds a wide range of applications in various domains, including aerial swarms, autonomous warehouse robotics, and self-driving vehicles. Current approaches to MAPF generally fall into two main categories: centralized and decentralized planning. Centralized planning suffers from the curse of dimensionality when the number of agents or states increases and thus does not scale well in large and complex environments. On the other hand, decentralized planning enables agents to engage in real-time path planning within a partially observable environment, demonstrating implicit coordination. However, they suffer from slow convergence and performance degradation in dense environments. In this paper, we introduce CRAMP, a novel crowd-aware decentralized reinforcement learning approach to address this problem by enabling efficient local communication among agents via Graph Neural Networks (GNNs), facilitating situational awareness and decision-making capabilities in congested environments. We test CRAMP on simulated environments and demonstrate that our method outperforms the state-of-the-art decentralized methods for MAPF on various metrics. CRAMP improves the solution quality up to 59% measured in makespan and collision count, and up to 35% improvement in success rate in comparison to previous methods.

翻译：多智能体路径规划在拥挤环境中是一个具有挑战性的运动规划问题，旨在为系统中所有智能体寻找无碰撞路径。该问题在多个领域具有广泛应用，包括空中集群、自主仓储机器人和自动驾驶车辆。当前多智能体路径规划方法主要分为集中式与分布式两类：集中式规划在智能体数量或状态增加时面临维度灾难问题，难以在复杂大规模环境中扩展；而分布式规划虽能使智能体在部分可观测环境下进行实时路径规划并展现隐式协调能力，但在密集环境中存在收敛速度慢和性能下降的问题。本文提出CRAMP——一种新颖的群体感知分布式强化学习方法，通过图神经网络实现智能体间高效局部通信，增强密集环境中的态势感知与决策能力。在仿真环境中的测试表明，我们的方法在多组评估指标上均优于现有最先进的分布式多智能体路径规划方法。与已有方法相比，CRAMP在解质量方面提升最高达59%（以完工时间和碰撞次数衡量），成功率提升最高达35%。