Hierarchical Reinforcement Learning with Automatic Curriculum Generation for Unmanned Combat Aerial Vehicle Tactical Decision-Making in Autonomous Air Combat

Hierarchical Reinforcement Learning with Automatic Curriculum Generation for Unmanned Combat Aerial Vehicle Tactical Decision-Making in Autonomous Air Combat

This study proposes an unmanned combat aerial vehicle (UCAV)-oriented hierarchical reinforcement learning framework to address the temporal abstraction challenge in autonomous within-visual-range air combat (WVRAC) for UCAVs. The incorporation of maximum-entropy objectives within the MEOL framework...

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Bibliographic Details
Main Authors: Yang Li, Wenhan Dong, Pin Zhang, Hengang Zhai, Guangqi Li
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Drones
Subjects:
unmanned combat aerial vehicles
within-visual-range air combat
hierarchical reinforcement learning
Wasserstein
maximum-entropy learning framework
temporal abstraction
Online Access:https://www.mdpi.com/2504-446X/9/5/384
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