Continuous Action Air Combat Maneuver Decision-Making Based on T-MGMM

In autonomous air combat, tactics are inherently complex, and control inputs are continuous. Traditional reinforcement learning (RL) algorithms often rely on discretization or independent Gaussian assumptions, which fail to capture correlations between control variables, limiting the expressiveness...

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主要な著者:	Junzhe Jiang, Hongming Wang, Zhixing Huang, Zhuangfeng Zhou, Xiang Wu, Wenqin Deng, Xueyun Chen
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2024-01-01
シリーズ:	IEEE Access
主題:	Air combat deep reinforcement learning maneuver decision-making continuous action space Transformer Gaussian mixture model
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10771757/
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