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
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 T...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/12/7/635 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714491461206016 |
|---|---|
| author | Penghui Xu Yu Zhang Le Hao Qilin Yan |
| author_facet | Penghui Xu Yu Zhang Le Hao Qilin Yan |
| author_sort | Penghui Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-45d5333203bc44119bb567d67ff37d19 |
| institution | DOAJ |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-45d5333203bc44119bb567d67ff37d192025-08-20T03:13:42ZengMDPI AGAerospace2226-43102025-07-0112763510.3390/aerospace12070635DETEAMSK: 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 TaskPenghui Xu0Yu Zhang1Le Hao2Qilin Yan3College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaThe 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.https://www.mdpi.com/2226-4310/12/7/635multiagent drone systemintelligent planning and decisionad hoc teamworkmodel-based reinforcement learningmodel-free reinforcement learningdynamic identification with decoupling |
| spellingShingle | Penghui Xu Yu Zhang Le Hao Qilin Yan 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 multiagent drone system intelligent planning and decision ad hoc teamwork model-based reinforcement learning model-free reinforcement learning dynamic identification with decoupling |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | multiagent drone system intelligent planning and decision ad hoc teamwork model-based reinforcement learning model-free reinforcement learning dynamic identification with decoupling |
| url | https://www.mdpi.com/2226-4310/12/7/635 |
| work_keys_str_mv | AT penghuixu deteamskamodelbasedreinforcementlearningapproachtointelligenttoplevelplanninganddecisionsformultidroneadhocteamworkbydecouplingtheidentificationofteammateandtask AT yuzhang deteamskamodelbasedreinforcementlearningapproachtointelligenttoplevelplanninganddecisionsformultidroneadhocteamworkbydecouplingtheidentificationofteammateandtask AT lehao deteamskamodelbasedreinforcementlearningapproachtointelligenttoplevelplanninganddecisionsformultidroneadhocteamworkbydecouplingtheidentificationofteammateandtask AT qilinyan deteamskamodelbasedreinforcementlearningapproachtointelligenttoplevelplanninganddecisionsformultidroneadhocteamworkbydecouplingtheidentificationofteammateandtask |