Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges
The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially inthe last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modelingthe problem....
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/11072350/ |
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| _version_ | 1849772826993623040 |
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| author | Tiziana Cattai Francesco Frattolillo Andrea Lacava Prasanna Raut Jennifer Simonjan Salvatore D'Oro Tommaso Melodia Evgenii Vinogradov Enrico Natalizio Stefania Colonnese Francesca Cuomo Luca Iocchi |
| author_facet | Tiziana Cattai Francesco Frattolillo Andrea Lacava Prasanna Raut Jennifer Simonjan Salvatore D'Oro Tommaso Melodia Evgenii Vinogradov Enrico Natalizio Stefania Colonnese Francesca Cuomo Luca Iocchi |
| author_sort | Tiziana Cattai |
| collection | DOAJ |
| description | The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially inthe last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modelingthe problem. However, it is often applied to abstractions of the original problem, thus leaving to next development phases the integration of RL solutions to the actual systems. This choice may not guarantee the overall optimal performance of the implemented system. In this survey, we analyze the literature on Multi-UAV applications that utilize reinforcement learning, with particular attention to works that consider realistic communication channels. We focus on identifying the key variables that influence communication and whether these variables are integrated within the RL framework or considered externally. Additionally, we identify key trends, challenges, and future directions in the field, providing a comprehensive overview for researchers and practitioners interested in the practical deployment of RL-based Multi-UAV systems. |
| format | Article |
| id | doaj-art-5fa26456110f4fa39f7b7e6dc17a134c |
| institution | DOAJ |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-5fa26456110f4fa39f7b7e6dc17a134c2025-08-20T03:02:14ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0162067208110.1109/OJVT.2025.358677411072350Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and ChallengesTiziana Cattai0https://orcid.org/0000-0002-6128-2246Francesco Frattolillo1https://orcid.org/0000-0002-2040-3355Andrea Lacava2https://orcid.org/0000-0001-9217-5956Prasanna Raut3https://orcid.org/0000-0002-3489-7405Jennifer Simonjan4https://orcid.org/0000-0002-2735-957XSalvatore D'Oro5https://orcid.org/0000-0002-7690-0449Tommaso Melodia6https://orcid.org/0000-0002-2719-1789Evgenii Vinogradov7https://orcid.org/0000-0002-4156-0317Enrico Natalizio8https://orcid.org/0000-0001-8553-5722Stefania Colonnese9https://orcid.org/0000-0002-1807-2155Francesca Cuomo10https://orcid.org/0000-0002-9122-7993Luca Iocchi11https://orcid.org/0000-0001-9057-8946Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, ItalyDepartment of Computer, Control and Management Engineering Sapienza University of Rome, Rome, ItalyDepartment of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, ItalyTechnology Innovation Institute Abu Dhabi, Abu Dhabi, UAETechnology Innovation Institute Abu Dhabi, Abu Dhabi, UAEInstitute for the Wireless Internet of Things, Northeastern University, Boston, MA, USAInstitute for the Wireless Internet of Things, Northeastern University, Boston, MA, USATechnology Innovation Institute Abu Dhabi, Abu Dhabi, UAETechnology Innovation Institute Abu Dhabi, Abu Dhabi, UAEDepartment of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, ItalyDepartment of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, ItalyDepartment of Computer, Control and Management Engineering Sapienza University of Rome, Rome, ItalyThe interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially inthe last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modelingthe problem. However, it is often applied to abstractions of the original problem, thus leaving to next development phases the integration of RL solutions to the actual systems. This choice may not guarantee the overall optimal performance of the implemented system. In this survey, we analyze the literature on Multi-UAV applications that utilize reinforcement learning, with particular attention to works that consider realistic communication channels. We focus on identifying the key variables that influence communication and whether these variables are integrated within the RL framework or considered externally. Additionally, we identify key trends, challenges, and future directions in the field, providing a comprehensive overview for researchers and practitioners interested in the practical deployment of RL-based Multi-UAV systems.https://ieeexplore.ieee.org/document/11072350/Multi-UAVreinforcement learning5Gcommunications |
| spellingShingle | Tiziana Cattai Francesco Frattolillo Andrea Lacava Prasanna Raut Jennifer Simonjan Salvatore D'Oro Tommaso Melodia Evgenii Vinogradov Enrico Natalizio Stefania Colonnese Francesca Cuomo Luca Iocchi Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges IEEE Open Journal of Vehicular Technology Multi-UAV reinforcement learning 5G communications |
| title | Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges |
| title_full | Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges |
| title_fullStr | Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges |
| title_full_unstemmed | Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges |
| title_short | Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges |
| title_sort | multi uav reinforcement learning with realistic communication models recent advances and challenges |
| topic | Multi-UAV reinforcement learning 5G communications |
| url | https://ieeexplore.ieee.org/document/11072350/ |
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