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Keywords = Ex-MADDPG

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21 pages, 1043 KB  
Article
Task Assignment of UAV Swarms Based on Deep Reinforcement Learning
by Bo Liu, Shulei Wang, Qinghua Li, Xinyang Zhao, Yunqing Pan and Changhong Wang
Drones 2023, 7(5), 297; https://doi.org/10.3390/drones7050297 - 29 Apr 2023
Cited by 21 | Viewed by 8221
Abstract
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 [...] Read more.
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. Full article
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