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Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality

Chenning Yu*,1, Qingbiao Li*,2, Sicun Gao1, and Amanda Prorok2
1 UC San Diego     2 University of Cambridge
Paper Video Code

Graph Transformer serves a heuristic function to accelerate multi-agent planning.

Abstract

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.

Maze Environments

Box Environments

BibTeX

@inproceedings{yu2023gnnsearch,
      title={Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality},
      author={Yu, Chenning and Li, Qingbiao and Gao, Sicun and Prorok, Amanda},
      booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
      year={2023},
      organization={IEEE}
    }