Efficient Deep Learning for Multi Agent Pathfinding

AAAI 2022 pp. 13122-13123

doi:10.1609/AAAI.V36I11.21697 /aaai/2022/abreu2022aaai-efficient/

Abstract

Multi Agent Path Finding (MAPF) is widely needed to coordinate real-world robotic systems. New approaches turn to deep learning to solve MAPF instances, primarily using reinforcement learning, which has high computational costs. We propose a supervised learning approach to solve MAPF instances using a smaller, less costly model.

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Cite

Text

Abreu. "Efficient Deep Learning for Multi Agent Pathfinding." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21697

Markdown

[Abreu. "Efficient Deep Learning for Multi Agent Pathfinding." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/abreu2022aaai-efficient/) doi:10.1609/AAAI.V36I11.21697

BibTeX

@inproceedings{abreu2022aaai-efficient,
  title     = {{Efficient Deep Learning for Multi Agent Pathfinding}},
  author    = {Abreu, Natalie},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13122-13123},
  doi       = {10.1609/AAAI.V36I11.21697},
  url       = {https://mlanthology.org/aaai/2022/abreu2022aaai-efficient/}
}