Multi-Agent Path Finding via Tree LSTM

Yuhao Jiang; Kunjie Zhang; Qimai Li; Jiaxin Chen; Xiaolong Zhu

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arXiv · 2210.12933

Multi-Agent Path Finding via Tree LSTM

Yuhao Jiang·Kunjie Zhang·Qimai Li·Jiaxin Chen·Xiaolong Zhu

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Abstract

In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL). However, in the 2021 Flatland3 Challenge, a competition on MAPF, the best RL method scored only 27.9, far less than the best OR method. This paper proposes a new RL solution to Flatland3 Challenge, which scores 125.3, several times higher than the best RL solution before. We creatively apply a novel network architecture, TreeLSTM, to MAPF in our solution. Together with several other RL techniques, including reward shaping, multiple-phase training, and centralized control, our solution is comparable to the top 2-3 OR methods.

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BibTeX RIS

Yuhao Jiang, Kunjie Zhang, Qimai Li, Jiaxin Chen, Xiaolong Zhu. 2022-10-24. Multi-Agent Path Finding via Tree LSTM. https://arxiv.org/abs/2210.12933

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