  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.
