DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task

Xu, Penghui; Zhang, Yu; Hao, Le; Yan, Qilin

doi:10.3390/aerospace12070635

Open AccessArticle

DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task

by

Penghui Xu

^*,

Yu Zhang

^*,

Le Hao

and

Qilin Yan

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China

^*

Authors to whom correspondence should be addressed.

Aerospace 2025, 12(7), 635; https://doi.org/10.3390/aerospace12070635

Submission received: 1 April 2025 / Revised: 2 July 2025 / Accepted: 14 July 2025 / Published: 16 July 2025

(This article belongs to the Special Issue Innovations in Unmanned Aerial Vehicle: Design and Development)

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Abstract

The ability to collaborate with new teammates, adapt to unfamiliar environments, and engage in effective planning is essential for multi-drone agents within unmanned combat systems. This paper introduces DETEAMSK (Model-based Reinforcement Learning by Decoupling the Identification of Teammates and Tasks), a model-based reinforcement learning method in intelligent top-level planning and decisions designed for ad hoc teamwork among multi-drone agents. It specifically addresses integrated reconnaissance and strike missions in urban combat scenarios under varying conditions. DETEAMSK’s performance is evaluated through comprehensive, multidimensional experiments and compared with other baseline models. The results demonstrate that DETEAMSK exhibits superior effectiveness, robustness, and generalization capabilities across a range of task domains. Moreover, the model-based reinforcement learning approach offers distinct advantages over traditional models, such as the PLASTIC-Model, and model-free approaches, like the PLASTIC-Policy, due to its unique “dynamic decoupling identification” feature. This study provides valuable insights for advancing both theoretical and applied research in model-based reinforcement learning methods for multi-drone systems.

Keywords: multiagent drone system; intelligent planning and decision; ad hoc teamwork; model-based reinforcement learning; model-free reinforcement learning; dynamic identification with decoupling

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MDPI and ACS Style

Xu, P.; Zhang, Y.; Hao, L.; Yan, Q. DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task. Aerospace 2025, 12, 635. https://doi.org/10.3390/aerospace12070635

AMA Style

Xu P, Zhang Y, Hao L, Yan Q. DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task. Aerospace. 2025; 12(7):635. https://doi.org/10.3390/aerospace12070635

Chicago/Turabian Style

Xu, Penghui, Yu Zhang, Le Hao, and Qilin Yan. 2025. "DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task" Aerospace 12, no. 7: 635. https://doi.org/10.3390/aerospace12070635

APA Style

Xu, P., Zhang, Y., Hao, L., & Yan, Q. (2025). DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task. Aerospace, 12(7), 635. https://doi.org/10.3390/aerospace12070635

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DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task

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