Recorded recurrent deep reinforcement learning guidance laws for intercepting endoatmospheric maneuvering missiles

Recorded recurrent deep reinforcement learning guidance laws for intercepting endoatmospheric maneuvering missiles

This work proposes a recorded recurrent twin delayed deep deterministic (RRTD3) policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise. The attack-defense engagement scenario is mod...

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Bibliographic Details
Main Authors: Xiaoqi Qiu, Peng Lai, Changsheng Gao, Wuxing Jing
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2024-01-01
Series:Defence Technology
Subjects:
Endoatmospheric interception
Missile guidance
Reinforcement learning
Markov decision process
Recurrent neural networks
Online Access:http://www.sciencedirect.com/science/article/pii/S2214914723000442

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http://www.sciencedirect.com/science/article/pii/S2214914723000442

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