Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

被引:6
|
作者
Liu, Bo [1 ]
Wang, Shulei [1 ]
Li, Qinghua [1 ]
Zhao, Xinyang [1 ]
Pan, Yunqing [1 ]
Wang, Changhong [1 ]
机构
[1] Harbin Inst Technol, Space Control & Inertial Technol Res Ctr, Harbin 150000, Peoples R China
关键词
UAV swarm; task assignment; deep reinforcement learning; Ex-MADDPG; ALLOCATION;
D O I
10.3390/drones7050297
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
UAV swarm applications are critical for the future, and their mission-planning and decision-making capabilities have a direct impact on their performance. However, creating a dynamic and scalable assignment algorithm that can be applied to various groups and tasks is a significant challenge. To address this issue, we propose the Extensible Multi-Agent Deep Deterministic Policy Gradient (Ex-MADDPG) algorithm, which builds on the MADDPG framework. The Ex-MADDPG algorithm improves the robustness and scalability of the assignment algorithm by incorporating local communication, mean simulation observation, a synchronous parameter-training mechanism, and a scalable multiple-decision mechanism. Our approach has been validated for effectiveness and scalability through both simulation experiments in the Multi-Agent Particle Environment (MPE) and a real-world experiment. Overall, our results demonstrate that the Ex-MADDPG algorithm is effective in handling various groups and tasks and can scale well as the swarm size increases. Therefore, our algorithm holds great promise for mission planning and decision-making in UAV swarm applications.
引用
收藏
页数:21
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