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This paper presents an autonomous decision-making framework for drone swarms using deep reinforcement learning, dividing UAVs into “reconnaissance” and “executive” roles. The framework includes a situational communication layer, task decision and planning layers, and a control layer. It employs a semi-centralized, semi-distributed mode in the communication layer and integrates Deep Deterministic Policy Gradient (DDPG) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in the decision and planning layers. An enhanced MADDPG algorithm is developed, resulting in a drone swarm autonomous decision-making method based on deep reinforcement learning. Simulations show that this approach outperforms in UAV swarm tasks.

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Autonomous Decision-Making of Drone Swarm Based on Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Na Zhang,
  • Shuhan Chen,
  • Shixun Xiong,
  • Fengying Zhang

摘要

This paper presents an autonomous decision-making framework for drone swarms using deep reinforcement learning, dividing UAVs into “reconnaissance” and “executive” roles. The framework includes a situational communication layer, task decision and planning layers, and a control layer. It employs a semi-centralized, semi-distributed mode in the communication layer and integrates Deep Deterministic Policy Gradient (DDPG) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in the decision and planning layers. An enhanced MADDPG algorithm is developed, resulting in a drone swarm autonomous decision-making method based on deep reinforcement learning. Simulations show that this approach outperforms in UAV swarm tasks.