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Learning-based fast alternating direction method of multipliers for multi-agent path finding using temporary variable-fixing
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AI
DOI：10.1016/j.ejor.2026.04.028.png)
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Abstract
En 中文
• We propose a novel learning-based fast ADMM for MAPF. • Fast ADMM is developed using tailored Dijkstra and soft start. • Imitation learning is used to build a policy network to accelerate fast ADMM. • Our method achieves considerable speedup with only a tiny loss in solution quality.
Keywords:
multi-agent path finding
alternating direction method of multipliers
imitation learning
fast ADMM
temporary variable-fixing
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6
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2.2W
Citations:
6.4W
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