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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Autores principales:	Junzhe Jiang, Hongming Wang, Zhixing Huang, Zhuangfeng Zhou, Xiang Wu, Wenqin Deng, Xueyun Chen
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2024-01-01
Colección:	IEEE Access
Materias:	Air combat deep reinforcement learning maneuver decision-making continuous action space Transformer Gaussian mixture model
Acceso en línea:	https://ieeexplore.ieee.org/document/10771757/
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