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Review

Drone Deep Reinforcement Learning: A Review

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College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
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Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
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School of Engineering and Applied Sciences, Nile University Campus, Sheikh Zayed District, Juhayna Square, 6th of October City, Giza 60411, Egypt
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Smart Engineering Systems Research Center (SESC), Nile University, Sheikh Zayed City, Giza 12588, Egypt
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Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150080, China
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General Motors Canada, 500 Wentworth St W, Oshawa, ON L1J 6J2, Canada
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Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Larsgårdsvegen, 2, 6009 Ålesund, Norway
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Department of Informatics, University of Bari, 70125 Bari, Italy
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Author to whom correspondence should be addressed.
Academic Editors: Mohamed Benbouzid and Juan M. Corchado
Electronics 2021, 10(9), 999; https://doi.org/10.3390/electronics10090999
Received: 5 March 2021 / Revised: 2 April 2021 / Accepted: 17 April 2021 / Published: 22 April 2021
Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios. View Full-Text
Keywords: unmanned aerial vehicles; UAVs; guidance; navigation; control; machine learning; deep reinforcement learning (DRL); literature review unmanned aerial vehicles; UAVs; guidance; navigation; control; machine learning; deep reinforcement learning (DRL); literature review
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MDPI and ACS Style

Azar, A.T.; Koubaa, A.; Ali Mohamed, N.; Ibrahim, H.A.; Ibrahim, Z.F.; Kazim, M.; Ammar, A.; Benjdira, B.; Khamis, A.M.; Hameed, I.A.; Casalino, G. Drone Deep Reinforcement Learning: A Review. Electronics 2021, 10, 999. https://doi.org/10.3390/electronics10090999

AMA Style

Azar AT, Koubaa A, Ali Mohamed N, Ibrahim HA, Ibrahim ZF, Kazim M, Ammar A, Benjdira B, Khamis AM, Hameed IA, Casalino G. Drone Deep Reinforcement Learning: A Review. Electronics. 2021; 10(9):999. https://doi.org/10.3390/electronics10090999

Chicago/Turabian Style

Azar, Ahmad Taher, Anis Koubaa, Nada Ali Mohamed, Habiba A. Ibrahim, Zahra Fathy Ibrahim, Muhammad Kazim, Adel Ammar, Bilel Benjdira, Alaa M. Khamis, Ibrahim A. Hameed, and Gabriella Casalino. 2021. "Drone Deep Reinforcement Learning: A Review" Electronics 10, no. 9: 999. https://doi.org/10.3390/electronics10090999

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