同济大学学报（自然科学版）2026，Vol.54Issue(7)：1005-1014,10.DOI:10.11908/j.issn.0253-374x.25131
基于多智能体强化学习的施工班组协同任务分配方法
Collaborative Task Allocation Method for Construction Crews Based on Multi-agent Reinforcement Learning
摘要
Abstract
To address the poor allocation capability and weak coordination among crews during construction,a multi-agent proximal policy optimization(MAPPO)-based method for collaborative task allocation was proposed.A multi-agent construction simulation environment was first developed using Unity 3D.A Markov decision process model was then formulated,and a MAPPO-based framework with centralized training and decentralized execution was designed.Additionally,a valid action filtering(VAF)mechanism for construction tasks and a progress-based reward shaping(PBRS)method were proposed,enabling the efficient environmental exploration by agents and addressing the challenges of variable action spaces and sparse rewards in task allocation.Case study results demonstrate that the proposed method reduces the project duration by 14.8%and improves the labor utilization by 9.7%compared to conventional multi-agent methods.Furthermore,comparative analysis between MAPPO and independent proximal policy optimization(IPPO)demonstrates the necessity of collaborative decision-making for task allocation.关键词
土木工程施工/施工仿真/任务分配/多智能体强化学习/多智能体系统Key words
civil engineering construction/construction simulation/task allocation/multi-agent reinforcement learning/multi-agent system分类
建筑与水利引用本文复制引用
王一凡,杨彬,汪丛军,刘伯达..基于多智能体强化学习的施工班组协同任务分配方法[J].同济大学学报（自然科学版）,2026,54(7):1005-1014,10.基金项目
国家重点研发计划(2024YFD1600402) （2024YFD1600402）
