强化学习
计算机科学
可靠性
可靠性(半导体)
人工智能
集合(抽象数据类型)
替代模型
透视图(图形)
样品(材料)
机器学习
数据挖掘
功率(物理)
化学
物理
色谱法
量子力学
政治学
法学
程序设计语言
作者
An J,Xuechao Zhang,Wei Liu,Wanting Rong
摘要
Based on the perspective of combining qualitative analysis and quantitative calculation, a method of operational concept capability requirement analysis is designed based on deep reinforcement learning. Firstly, it obtains the simulation small sample data set with high reliability based on simulation experiment. Secondly, the operational concept surrogate model is constructed on the empirical data, and the surrogate model is optimized and trained by using the multi-objective optimization algorithm with high credibility simulation data set as input. Finally, the trained surrogate model is interacted with the deep reinforcement learning framework to realize the reverse exploration of operational concept capability requirements.
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