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

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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Bibliographic Details
Main Authors: Junzhe Jiang, Hongming Wang, Zhixing Huang, Zhuangfeng Zhou, Xiang Wu, Wenqin Deng, Xueyun Chen
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Air combat
deep reinforcement learning
maneuver decision-making
continuous action space
Transformer
Gaussian mixture model
Online Access:https://ieeexplore.ieee.org/document/10771757/
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