航空学报 > 2023, Vol. 44 Issue (S2): 729770-729770   doi: 10.7527/S1000-6893.2023.29770

多智能体多耦合任务混合式智能决策架构设计

王雪鉴, 文永明, 石晓荣(), 张宁宁, 刘洁玺   

  1. 北京控制与电子技术研究所,北京 100038
  • 收稿日期:2023-10-26 修回日期:2023-11-21 接受日期:2023-12-20 出版日期:2023-12-25 发布日期:2024-01-04
  • 通讯作者: 石晓荣 E-mail:mely0110@sina.com

Design of hybrid intelligent decision framework for multi⁃agent and multi⁃coupling tasks

Xuejian WANG, Yongming WEN, Xiaorong SHI(), Ningning ZHANG, Jiexi LIU   

  1. Beijing Institute of Control & Electronics Technology,Beijing 100038,China
  • Received:2023-10-26 Revised:2023-11-21 Accepted:2023-12-20 Online:2023-12-25 Published:2024-01-04
  • Contact: Xiaorong SHI E-mail:mely0110@sina.com

摘要:

针对多智能体在实际复杂应用场景下面对的任务分配和路径规划等多任务相互耦合问题及其决策问题,提出了一种多智能体多耦合任务混合式智能决策架构设计方法。首先,结合单智能体多任务混合式架构和多智能体分布式协同控制的优点,设计了多智能体面向多耦合任务的混合式智能决策架构;其次,对架构的策略网络以及策略网络训练控制器进行设计,并提出了基于耦合关系的耦合关系矩阵,实现了多智能体多任务在面对协同决策问题时的高效训练;最后,在仿真环境下进行建模、算法训练与仿真,并通过与传统方法进行对比试验,验证了所提方法的有效性和先进性。

关键词: 智能决策, 深度强化学习, 多智能体, 路径规划, 任务分配

Abstract:

To address the coupling problem and decision-making problem of multiple tasks such as task allocation and path planning of multi-agents in complex application scenarios, a design method of hybrid intelligent decision-making framework for multi-agent and multi-coupling tasks is proposed. Firstly, the advantages of single agent multi-task hybrid framework and multi-agent distributed collaborative control, a hybrid intelligent decision-making framework for multi-agent and multi-coupling tasks is designed. Secondly, the strategy network of the framework and the training controller for the strategy network are designed and a coupling relationship matrix based on coupling relationships is proposed to achieve efficient training of multi-agents and multi-tasks in face of collaborative decision-making problems. Finally, this paper modeled, trained algorithm and simulated in simulation environment,and compared with the tradition method to verifies the effectiveness and advantages of the proposed method.

Key words: intelligent decision, deep reinforcement learning, multi-agent, path planning, task allocation

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