  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics that
asks us to compute collision-free paths for a team of agents, all moving across
a shared map. Although many works appear on this topic, all current algorithms
struggle as the number of agents grows. The principal reason is that existing
approaches typically plan free-flow optimal paths, which creates congestion. To
tackle this issue, we propose a new approach for MAPF where agents are guided
to their destination by following congestion-avoiding paths. We evaluate the
idea in two large-scale settings: one-shot MAPF, where each agent has a single
destination, and lifelong MAPF, where agents are continuously assigned new
destinations. Empirically, we report large improvements in solution quality for
one-short MAPF and in overall throughput for lifelong MAPF.
