@inproceedings{3adb1a47d0d34e9096fdbfaeff3d82e7,
title = "Autonomous UAVs in Disaster Management: A Survey on DRL-Driven Approaches",
abstract = "Unmanned aerial vehicles (UAVs) have become essential in disaster management, aiding in search and rescue, damage assessment, supply delivery, and communication restoration. However, traditional UAV control methods struggle with real-time decision-making and adaptability in dynamic disaster scenarios. Deep reinforcement learning (DRL) offers a promising solution by enabling UAVs to learn optimal strategies in uncertain and complex environments autonomously. This paper explores the advantages and limitations of deploying UAVs in disaster scenarios, outlines the core principles and applications of DRL in autonomous systems, and investigates research efforts that combine these technologies to address specific disaster response tasks. Finally, it synthesizes the gathered information to identify current issues and potential future directions for research.",
keywords = "Deep reinforcement learning (DRL), Disaster management, Real-time decision-making, Unmanned aerial vehicles (UAVs)",
author = "Nguyen, \{Tri Hai\} and Nguyen, \{Huy T.\} and Bui, \{Minh Phung\} and Nguyen, \{Luong Vuong\} and Laihyuk Park and Bao, \{Vo Nguyen Quoc\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 1st International Conference on Intelligent Aerial Access and Applications, IAAA 2025 ; Conference date: 16-07-2025 Through 18-07-2025",
year = "2026",
doi = "10.1007/978-3-032-14935-0\_4",
language = "English",
isbn = "9783032149343",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "43--56",
editor = "Nhu-Ngoc Dao and Quang-Dung Pham and Le, \{Hong Anh\} and Thinh, \{Tran Ngoc\}",
booktitle = "Intelligent Aerial Access and Applications Towards 6G and Beyond - International Conference, IAAA 2025, Proceedings",
}