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.
关键词
深度强化学习/层次化多智能体/信息共享/智能兵棋推演
Key words
deep reinforcement learning/hierarchical multi-agent/information sharing/intelligent war-gaming simulation
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