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Conflict-Based Search is one of the most popular methods for multi-agent pathfinding. Though it is complete and optimal, it does not scale well. Recentworks have been proposed to accelerate it by introducing various heuristics.However, whether these heuristics can apply to non-grid-based problem settingswhile 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 alearning-based component, i.e., the Graph Transformer, as a heuristic functionto accelerate the planning. The proposed method is provably complete andbounded-suboptimal with any desired factor. We conduct extensive experiments ontwo environments with dense graphs. Results show that the proposed GraphTransformer can be trained in problem instances with relatively few agents andgeneralizes well to a larger number of agents, while achieving betterperformance than state-of-the-art methods.

Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality