  Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems.
Recent methods have applied reinforcement learning (RL) to learn decentralized
polices in partially observable environments. A fundamental challenge of
obtaining collision-free policy is that agents need to learn cooperation to
handle congested situations. This paper combines communication with deep
Q-learning to provide a novel learning based method for MAPF, where agents
achieve cooperation via graph convolution. To guide RL algorithm on
long-horizon goal-oriented tasks, we embed the potential choices of shortest
paths from single source as heuristic guidance instead of using a specific path
as in most existing works. Our method treats each agent independently and
trains the model from a single agent's perspective. The final trained policy is
applied to each agent for decentralized execution. The whole system is
distributed during training and is trained under a curriculum learning
strategy. Empirical evaluation in obstacle-rich environment indicates the high
success rate with low average step of our method.
