High-Speed Three-Dimensional Aerial Vehicle Evasion Based on a Multi-Stage Dueling Deep Q-Network

This paper proposes a multi-stage dueling deep Q-network (MS-DDQN) algorithm to address the high-speed aerial vehicle evasion problem. High-speed aerial vehicle pursuit and evasion are an ongoing game attracting significant research attention in the field of autonomous aerial vehicle decision making...

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Bibliographic Details
Published inAerospace Vol. 9; no. 11; p. 673
Main Authors Yang, Yefeng, Huang, Tao, Wang, Xinxin, Wen, Chih-Yung, Huang, Xianlin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2022
Subjects
aerial vehicle evasion
Aircraft
Algorithms
Analysis
Decision making
deep reinforcement learning
Drone aircraft
dueling deep Q-network
Engines
Game theory
High speed
Hilbert space
multi-stage training
Pursuit-evasion games
Simulation
Unmanned aerial vehicles
Vehicles
Velocity
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Summary:This paper proposes a multi-stage dueling deep Q-network (MS-DDQN) algorithm to address the high-speed aerial vehicle evasion problem. High-speed aerial vehicle pursuit and evasion are an ongoing game attracting significant research attention in the field of autonomous aerial vehicle decision making. However, traditional maneuvering methods are usually not applicable in high-speed scenarios. Independent of the aerial vehicle model, the implemented MS-DDQN-based method searches for an approximate optimal maneuvering policy by iteratively interacting with the environment. Furthermore, the multi-stage learning mechanism was introduced to improve the training data quality. Simulation experiments were conducted to compare the proposed method with several typical evasion maneuvering policies and to reveal the effectiveness and robustness of the proposed MS-DDQN algorithm.
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace9110673