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In an adversarial environment, agent clusters encounter tasks with uncertain arrival times and structures. As the current agent clusters present the characteristics of multiple networks, the previous methods applied to single-network collaboration cannot adjust the strategy in time according to the current environment, which greatly reduces the collaborative effect. In this paper, we propose a multi-agent collaboration method for time-sensitive tasks in multiple networked confrontation environments. By using hierarchical reinforcement learning, the collaboration problem is divided into the network-agent layer and task-subtask layer. A lot of experiments and theoretical analysis show that the method proposed in this chapter can better accomplish the assignment between the task and the agent, and improve the task income.

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Multi-agent Collaboration for Time-Sensitive Tasks in Multiple Networked Adversarial Scenarios

  • 会议论文

  • 发表日期：

    2025年

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  • Jian Wu,
  • Yuanshuang Jiang,
  • Pan Li,
  • Xiangxiang Xing,
  • Kai Di,
  • Xin Wang,
  • Qiang Liu,
  • Liangping Cheng,
  • Yichuan Jiang,
  • Dan Chen

摘要

In an adversarial environment, agent clusters encounter tasks with uncertain arrival times and structures. As the current agent clusters present the characteristics of multiple networks, the previous methods applied to single-network collaboration cannot adjust the strategy in time according to the current environment, which greatly reduces the collaborative effect. In this paper, we propose a multi-agent collaboration method for time-sensitive tasks in multiple networked confrontation environments. By using hierarchical reinforcement learning, the collaboration problem is divided into the network-agent layer and task-subtask layer. A lot of experiments and theoretical analysis show that the method proposed in this chapter can better accomplish the assignment between the task and the agent, and improve the task income.