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Collaborative deep reinforcement learning algorithm for solving multi-AGV dynamic scheduling problem
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DOI：10.1016/j.eswa.2026.131941.png)
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Abstract
En 中文
• Novel CDRLA for dynamic AGV task allocation and path planning. • Collaborative search mechanism enabling two-agent synergy. • Path planning agent with novel reward and collision avoidance mechanism. • Task allocation agent with attention-based feature extraction. • CDRLA validated for efficiency in multi-scale instances.
Keywords:
CDRLA
multi-AGV scheduling
dynamic task allocation
path planning
deep reinforcement learning
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7.5
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3.0W
Citations:
10.2W
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Cited Papers
Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems
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