基于深度强化学习的作战概念能力需求分析

Capability requirement analysis for operational concept based on deep reinforcement learning

作战能力需求分析是作战概念开发过程中的关键环节,在形式化描述作战概念能力需求分析问题的基础上,通过定性与定量结合,设计了一种基于深度强化学习的作战概念能力需求分析方法。该方法通过模拟仿真实验,获取高可信度的仿真小样本数据集;基于经验数据构建作战概念的代理模型,并以高可信度仿真数据集为输入,应用多目标优化算法对代理模型进行优化训练;最后,将训练得到的代理模型与深度强化学习框架进行交互寻优,实现作战概念能力需求的反向探索。选取“超越式登陆”为实例进行验证,实验结果表明方法可行。

Capability requirement analysis is the key stage of operational concept development.Based on the formal description of the operational concept capability requirement analysis,a method of operational concept capability requirement analysis based on DRL(deep reinforcement learning)is designed from the perspective of qualitative and quantitative combination.In this method,small sample data sets with high reliability can be obtained through simulation experiments.Based on the experience data,the surrogate model of operation concept is constructed,and the model is optimized and trained by using multi-objective optimization algorithm with the high credibility simulation data set as the input.Finally,the agent model obtained from the training and the DRL framework are interactively optimized to achieve the reverse exploration of the operational concept capability requirements.The experiment results show that the method is feasible.