  Multi-agent pathfinding (MAPF) has been widely used to solve large-scale
real-world problems, e.g., automation warehouses. The learning-based, fully
decentralized framework has been introduced to alleviate real-time problems and
simultaneously pursue optimal planning policy. However, existing methods might
generate significantly more vertex conflicts (or collisions), which lead to a
low success rate or more makespan. In this paper, we propose a PrIoritized
COmmunication learning method (PICO), which incorporates the \textit{implicit}
planning priorities into the communication topology within the decentralized
multi-agent reinforcement learning framework. Assembling with the classic
coupled planners, the implicit priority learning module can be utilized to form
the dynamic communication topology, which also builds an effective
collision-avoiding mechanism. PICO performs significantly better in large-scale
MAPF tasks in success rates and collision rates than state-of-the-art
learning-based planners.
