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Research on Autonomous Manoeuvre Decision Making in Within-Visual-Range Aerial Two-Player Zero-Sum Games Based on Deep Reinforcement Learning

Author

Listed:
  • Bo Lu

    (Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China
    National Key Lab of Unmanned Aerial Vehicle Technology, Air Force Engineering University, Xi’an 710051, China)

  • Le Ru

    (Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China
    National Key Lab of Unmanned Aerial Vehicle Technology, Air Force Engineering University, Xi’an 710051, China)

  • Shiguang Hu

    (Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China
    National Key Lab of Unmanned Aerial Vehicle Technology, Air Force Engineering University, Xi’an 710051, China)

  • Wenfei Wang

    (Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China
    National Key Lab of Unmanned Aerial Vehicle Technology, Air Force Engineering University, Xi’an 710051, China)

  • Hailong Xi

    (Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China
    National Key Lab of Unmanned Aerial Vehicle Technology, Air Force Engineering University, Xi’an 710051, China)

  • Xiaolin Zhao

    (Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China
    National Key Lab of Unmanned Aerial Vehicle Technology, Air Force Engineering University, Xi’an 710051, China)

Abstract

In recent years, with the accelerated development of technology towards automation and intelligence, autonomous decision-making capabilities in unmanned systems are poised to play a crucial role in contemporary aerial two-player zero-sum games (TZSGs). Deep reinforcement learning (DRL) methods enable agents to make autonomous manoeuvring decisions. This paper focuses on current mainstream DRL algorithms based on fundamental tactical manoeuvres, selecting a typical aerial TZSG scenario—within visual range (WVR) combat. We model the key elements influencing the game using a Markov decision process (MDP) and demonstrate the mathematical foundation for implementing DRL. Leveraging high-fidelity simulation software (Warsim v1.0), we design a prototypical close-range aerial combat scenario. Utilizing this environment, we train mainstream DRL algorithms and analyse the training outcomes. The effectiveness of these algorithms in enabling agents to manoeuvre in aerial TZSG autonomously is summarised, providing a foundational basis for further research.

Suggested Citation

  • Bo Lu & Le Ru & Shiguang Hu & Wenfei Wang & Hailong Xi & Xiaolin Zhao, 2024. "Research on Autonomous Manoeuvre Decision Making in Within-Visual-Range Aerial Two-Player Zero-Sum Games Based on Deep Reinforcement Learning," Mathematics, MDPI, vol. 12(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2160-:d:1432234
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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