Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

Liu, Bo; Wang, Shulei; Li, Qinghua; Zhao, Xinyang; Pan, Yunqing; Wang, Changhong

doi:10.3390/drones7050297

Open AccessFeature PaperArticle

Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

by

Bo Liu

^†

,

Shulei Wang

^†,

Qinghua Li

,

Xinyang Zhao

,

Yunqing Pan

and

Changhong Wang

^*

Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150000, China

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Drones 2023, 7(5), 297; https://doi.org/10.3390/drones7050297

Submission received: 22 March 2023 / Revised: 21 April 2023 / Accepted: 28 April 2023 / Published: 29 April 2023

(This article belongs to the Special Issue Advanced Intelligent Decision-Making and Flight Control of Unmanned Aerial Vehicles)

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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 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.

Keywords: UAV swarm; task assignment; deep reinforcement learning; Ex-MADDPG

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MDPI and ACS Style

Liu, B.; Wang, S.; Li, Q.; Zhao, X.; Pan, Y.; Wang, C. Task Assignment of UAV Swarms Based on Deep Reinforcement Learning. Drones 2023, 7, 297. https://doi.org/10.3390/drones7050297

AMA Style

Liu B, Wang S, Li Q, Zhao X, Pan Y, Wang C. Task Assignment of UAV Swarms Based on Deep Reinforcement Learning. Drones. 2023; 7(5):297. https://doi.org/10.3390/drones7050297

Chicago/Turabian Style

Liu, Bo, Shulei Wang, Qinghua Li, Xinyang Zhao, Yunqing Pan, and Changhong Wang. 2023. "Task Assignment of UAV Swarms Based on Deep Reinforcement Learning" Drones 7, no. 5: 297. https://doi.org/10.3390/drones7050297

APA Style

Liu, B., Wang, S., Li, Q., Zhao, X., Pan, Y., & Wang, C. (2023). Task Assignment of UAV Swarms Based on Deep Reinforcement Learning. Drones, 7(5), 297. https://doi.org/10.3390/drones7050297

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Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

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