Enhancing Automated Maneuvering Decisions in UCAV Air Combat Games Using Homotopy-Based Reinforcement Learning

Enhancing Automated Maneuvering Decisions in UCAV Air Combat Games Using Homotopy-Based Reinforcement Learning

In the field of real-time autonomous decision-making for Unmanned Combat Aerial Vehicles (UCAVs), reinforcement learning is widely used to enhance their decision-making capabilities in high-dimensional spaces. These enhanced capabilities allow UCAVs to better respond to the maneuvers of various oppo...

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Bibliographic Details
Main Authors: Yiwen Zhu, Yuan Zheng, Wenya Wei, Zhou Fang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Drones
Subjects:
air combat game
homotopy method
reinforcement learning
self-play
sparse reward
Online Access:https://www.mdpi.com/2504-446X/8/12/756
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