郑州大学学报（工学版）2025，Vol.46Issue(3)：26-33,8.DOI:10.13705/j.issn.1671-6833.2025.03.001
基于多智能体强化学习的AMR协作任务分配方法
AMRs Autonomous Collaboration Task Assignment Method Based on Multi-agent Reinforcement Learning
摘要
Abstract
In order to solve the task autonomy assignment problem of AMR in flexible production,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on improved multi-agent reinforcement learning al-gorithm was adopted.The attention mechanism was introduced to improve the algorithm.Firstly,the framework of centralized training decentralized execution was adopted,and then the action and state of AMR were set.Secondly,according to the size of the reward value,the coverage degree of the task node and the completion effect of the task were determined.The simulation results showed that the average reward value of MADDPG algorithm increase 3 than other algorithms,and the training times were reduced by 300 times.It could have faster learning speed and more stable convergence process while ensuring the completion of task allocation.关键词
自主移动机器人/多智能体/强化学习/协作/任务分配Key words
autonomous mobile robot/multi-agent/reinforcement learning/collaboration/task assignment分类
计算机与自动化引用本文复制引用
张富强,张焱锐,丁凯,常丰田..基于多智能体强化学习的AMR协作任务分配方法[J].郑州大学学报（工学版）,2025,46(3):26-33,8.基金项目
国家重点研发计划项目(2021YFB3301702) （2021YFB3301702）
陕西省科技重大专项(2018zdzx01-01-01) （2018zdzx01-01-01）
