A Reinforcement Learning Method for Optimizing Air Combat Threat Assessment via Cross-attention Mechanisms and Expert-guided Reward Shaping

SUNKang, XUEDingrui, FANJi, LINYuqing, LIBo, WANGKexin, LIUJiancheng, WEISiwen

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  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
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Acta Armamentarii ›› 2025, Vol. 46 ›› Issue (S1) : 250606. DOI: 10.12382/bgxb.2025.0606

A Reinforcement Learning Method for Optimizing Air Combat Threat Assessment via Cross-attention Mechanisms and Expert-guided Reward Shaping

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{{article.zuoZheEn_L}}. {{article.title_en}}. {{journal.qiKanMingCheng_EN}}. 2025, 46(S1): 250606 https://doi.org/10.12382/bgxb.2025.0606

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