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Acta Armamentarii ›› 2024, Vol. 45 ›› Issue (10): 3385-3396.doi: 10.12382/bgxb.2023.0862

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A Hierarchical Multi-Agent Collaborative Decision-making Method Based on the Actor-critic Framework

FU Yanfang1, LEI Kailin1,*(), WEI Jianing2, CAO Zijian1, YANG Bo1, WANG Wei3, SUN Zelong4, LI Qinjie1   

  1. 1 School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, Shaanxi, China
    2 Beijing Electro-Mechanical Engineering Institute, Beijing 100083, China
    3 Unit 95810 of PLA, Beijing 100076, China
    4 School of Armament Science and Technology, Xi’an Technological University, Xi’an 710021, Shaanxi, China
  • Received:2023-09-05 Online:2024-10-28
  • Contact: LEI Kailin

Abstract:

A hierarchical multi-agent collaborative decision-making method based on the actor-critic (AC) frameworkis proposed to address the issues of improper task allocation and weak decision consistency in the collaborative decision-making of multiple agents in complex operational environments. The proposed method divides the decision-making process into different levels and utilizes the AC framework to facilitate information exchange and decision coordination among the agents, thereby enhancing thedecision efficiency and combat effectiveness. At the higher level, the top-level agents formulate thetask decisions by decomposing and assigning overall tasks to the lower-level agents. At the lower level, the lower-level agents make action decisions based on subtasks and provide feedback to the higher level. Experimental results demonstrate that the proposed method performs well in various operational simulation scenarios, showcasing its potential to enhance themilitary operational collaborative decision-making capability.

Key words: deep reinforcement learning, hierarchical multi-agent, information sharing, intelligent war-gaming simulation

CLC Number: