Reinforcement learning technology for air combat confrontation of unmanned aerial vehicle

Huan Zhou; Xiaoyan Zhang; Zhuoran Zhang

doi:10.1117/12.2631651

18 March 2022 Reinforcement learning technology for air combat confrontation of unmanned aerial vehicle

Huan Zhou, Xiaoyan Zhang, Zhuoran Zhang

Author Affiliations +

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121681Y (2022) https://doi.org/10.1117/12.2631651
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

UAV air combat game confrontation requires a high level of intelligence and autonomy, and needs the support of artificial intelligence technology to effectively improve the ability of autonomous air combat. This paper summarizes the reinforcement learning methods for UAV air combat confrontation. Firstly, the problem and application background of UAV air combat confrontation are introduced. Then, the research status of reinforcement learning methods at home and abroad is analyzed, including basic reinforcement learning, reinforcement learning based on Markov chain and deep reinforcement learning algorithm. On this basis, this paper focuses on the reinforcement learning method of UAV air combat confrontation from the two aspects of existing research results and algorithm application advantages. Finally, the simulation is implemented, and results show the effectiveness of reinforcement learning algorithm in air combat confrontation.

Citation Download Citation

Huan Zhou, Xiaoyan Zhang, and Zhuoran Zhang "Reinforcement learning technology for air combat confrontation of unmanned aerial vehicle", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121681Y (18 March 2022); https://doi.org/10.1117/12.2631651

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KEYWORDS

Unmanned aerial vehicles

Evolutionary algorithms

Neural networks

Artificial intelligence

Detection and tracking algorithms

Analytical research

Computer simulations

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