A new approach for drone tracking with drone using Proximal Policy Optimization based distributed deep reinforcement learning
Yazarlar (2)
Dr. Öğr. Üyesi Ziya TAN
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Fırat Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
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| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Elsevier BV SoftwareX |
| Dergi ISSN | 2352-7110 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 07-2023 |
| Cilt No | 23 |
| Sayı | 101497 |
| Sayfalar | 1 / 8 |
| DOI Numarası | 10.1016/j.softx.2023.101497 |
| Makale Linki | http://dx.doi.org/10.1016/j.softx.2023.101497 |
| Özet |
| In this paper, a distributed deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed for an unmanned aerial vehicle (UAV) to autonomously track another UAV. Accordingly, this paper makes three important contributions to the literature. The first one is the development of an efficient UAV tracking algorithm, the second one is the presentation of a deep reinforcement learning approach that can be adapted to the problem, and the third one is the introduction of a generalized distributed deep reinforcement learning platform that can be used in various problems such as tracking, control and mission coordination of UAVs. In order to validate the developed approaches, the PPO algorithm is simulated with the deep reinforcement learning algorithm in a distributed and non-distributed manner, a follower UAV is trained in different scenarios and the distributed and non-distributed … |
| Anahtar Kelimeler |
| Distributed learning | Drone tracking | Reinforcement learning | Proximal Policy Optimization |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 10 |
| Google Scholar | 17 |