The Effects of Rewards on Autonomous Unmanned Aerial Vehicle (UAV) Operations Using Reinforcement Learning

被引:4
|
作者
Virani, Hemali [1 ]
Liu, Dahai [2 ]
Vincenzi, Dennis [3 ]
机构
[1] Embry Riddle Aeronaut Univ, Dept Human Factors & Syst, 1 Aerosp Blvd, Daytona Beach, FL 32114 USA
[2] Embry Riddle Aeronaut Univ, Sch Grad Studies, 1 Aerosp Blvd, Daytona Beach, FL 32114 USA
[3] Embry Riddle Aeronaut Univ, Dept Grad Studies, 1 Aerosp Blvd, Daytona Beach, FL 32114 USA
关键词
UAV; reinforcement learning; reward scheme;
D O I
10.1142/S2301385021500187
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The effects of rewards on the ability of an autonomous UAV controlled by a Reinforcement Learning agent to accomplish a target localization task were investigated. It was shown that with an increase in the reward obtained by a learning agent upon correct detection, systems would become more risk-tolerant, efficient and have a tendency to locate targets faster with an increase in the sensor sensitivity after systems achieve steady-state performance.
引用
收藏
页码:349 / 360
页数:12
相关论文
共 50 条
  • [1] Autonomous Unmanned Aerial Vehicle navigation using Reinforcement Learning: A systematic review
    AlMahamid, Fadi
    Grolinger, Katarina
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 115
  • [2] Vision-Based Deep Reinforcement Learning of Unmanned Aerial Vehicle (UAV) Autonomous Navigation Using Privileged Information
    Wang, Junqiao
    Yu, Zhongliang
    Zhou, Dong
    Shi, Jiaqi
    Deng, Runran
    DRONES, 2024, 8 (12)
  • [3] Autonomous control of unmanned aerial vehicle for chemical detection using deep reinforcement learning
    Byun, Hyung Joon
    Nam, Hyunwoo
    ELECTRONICS LETTERS, 2022, 58 (11) : 423 - 425
  • [4] Omnidirectional Autonomous Aggressive Perching of Unmanned Aerial Vehicle using Reinforcement Learning Trajectory Generation and Control
    Huang, Yu-Ting
    Pi, Chen-Huan
    Cheng, Stone
    2022 JOINT 12TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS AND 23RD INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (SCIS&ISIS), 2022,
  • [5] Vision-Based Autonomous Landing of a Multi-Copter Unmanned Aerial Vehicle using Reinforcement Learning
    Lee, Seongheon
    Shim, Taemin
    Kim, Sungjoong
    Park, Junwoo
    Hong, Kyungwoo
    Bang, Hyochoong
    2018 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS), 2018, : 108 - 114
  • [6] Deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3D Hand Gestures
    Khan, Fawad Salam
    Mohd, Mohd Norzali Haji
    Zulkifli, Saiful Azrin B. M.
    Abro, Ghulam E. Mustafa
    Kazi, Suhail
    Soomro, Dur Muhammad
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 72 (03): : 5741 - 5759
  • [7] Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking
    Zhu, Yujiao
    Chen, Mingzhe
    Wang, Sihua
    Hu, Ye
    Liu, Yuchen
    Yin, Changchuan
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2024, 23 (12) : 10787 - 10802
  • [8] An autonomous aerial vehicle for unmanned security and surveillance operations: design and test
    Belloni, G.
    Feroli, A.
    Ficola, A.
    Pagnottelli, S.
    Valigi, P.
    2007 IEEE INTERNATIONAL WORKSHOP ON SAFETY, SECURITY AND RESCUE ROBOTICS, 2007, : 179 - 182
  • [9] VISION-BASED AUTONOMOUS INSPECTION OF VERTICAL STRUCTURES USING UNMANNED AERIAL VEHICLE (UAV)
    Gupta, Ayush
    Shukla, Amit
    Kumar, Amit
    Sivarathri, Ashok Kumar
    PROCEEDINGS OF ASME 2022 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, IMECE2022, VOL 3, 2022,
  • [10] Cooperative Planning for an Unmanned Combat Aerial Vehicle Fleet Using Reinforcement Learning
    Yuksek, Burak
    Demirezen, Mustafa Umut
    Inalhan, Gokhan
    Tsourdos, Antonios
    JOURNAL OF AEROSPACE INFORMATION SYSTEMS, 2021, 18 (10): : 739 - 750