Fast calculation of missile attack zone has always been a hot issue in engineering.With the development of machine learning,using neural network in deep learning to solve missile attack zone has become a hot topic.In order to deeply understand the application of machine learning in the calculation of missile attack zone,the calculation methods of attack zone at home and abroad are summarized.The research status of neural network solution algorithm based on deep learning is mainly introduced.The characteristics of various solution methods are compared and analyzed.On this basis,the application of machine learning in the calculation of attack zone in the future is prospected in combination with reinforcement learning.Using the method of comparison and theoretical analysis,considering the actual engineering level,an optimization strategy based on deep reinforcement learning is proposed,which provides theoretical guidance for the application of machine learning in the calculation of air-to-air missile attack zone.
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