Collaborative deep reinforcement learning for solving multi-objective vehicle routing problems

• Main Authors: WU, Yaoxin, FAN, Mingfeng, CAO, Zhiguang, GAO, Ruobin, HOU, Yaqing, SARTORETTI, Guillaume
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Multi-objective vehicle routing problems; Deep reinforcement learning; Attention network; Collaborative active search; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/9328; https://ink.library.smu.edu.sg/context/sis_research/article/10328/viewcontent/55_AAMAS2024_MOVRP.pdf
• Institution: Singapore Management University

Existing deep reinforcement learning (DRL) methods for multi-objective vehicle routing problems (MOVRPs) typically decompose an MOVRP into subproblems with respective preferences and then train policies to solve corresponding subproblems. However, such a paradigm is still less effective in tackling...