  Conflict-Based Search is one of the most popular methods for multi-agent path
finding. Though it is complete and optimal, it does not scale well. Recent
works have been proposed to accelerate it by introducing various heuristics.
However, whether these heuristics can apply to non-grid-based problem settings
while maintaining their effectiveness remains an open question. In this work,
we find that the answer is prone to be no. To this end, we propose a
learning-based component, i.e., the Graph Transformer, as a heuristic function
to accelerate the planning. The proposed method is provably complete and
bounded-suboptimal with any desired factor. We conduct extensive experiments on
two environments with dense graphs. Results show that the proposed Graph
Transformer can be trained in problem instances with relatively few agents and
generalizes well to a larger number of agents, while achieving better
performance than state-of-the-art methods.
