Related papers: Multi-Agent Path Finding via Tree LSTM

Multi-Agent Path Finding via Tree LSTM

URL: http://arxiv.org/abs/2210.12933v1
Date: Mon, 24 Oct 2022 03:22:20 GMT
Title: Multi-Agent Path Finding via Tree LSTM
Authors: Yuhao Jiang, Kunjie Zhang, Qimai Li, Jiaxin Chen, Xiaolong Zhu
Abstract summary: 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.
Score: 17.938710696964662
License: http://creativecommons.org/licenses/by/4.0/
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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