南京航空航天大学学报（英文版）2023，Vol.40Issue(1)：25-36,12.
基于深度强化学习的空海联合作战智能决策新方法
A New Intelligent Decision-Making Method for Air-Sea Joint Operation Based on Deep Reinforcement Learning
摘要
Abstract
Aiming at the difficulty of air-sea joint operation in complex multi-equipment combat with high uncertainty, a new intelligent decision-making method for air-sea joint operation based on deep reinforcement learning is proposed. To uniformly represent the input and output of complex networks and their corresponding relations, various networks are utilized, e. g., perceptron, deep long-short term memory network and actor critical structure. Aiming at the instability of policy network learning process and the defects of the proximal policy optimization(PPO) algorithm, an improved proximate policy optimization algorithm is proposed. To enhance the variability of opponent's strategy in the process of policy network self-learning, a baseline policy model selection method based on model performance and model diversity is proposed. The experiments demonstrate that the proposed method is effective and stable in air-sea joint operation decision. In the 4th Wargaming Competition hosted by Chinese Institute of Command and Control, the winning rate in more than 100 rounds against regular decision-making algorithm and human confrontation was 97％, which was about 20％ higher than that of regular decision-making algorithms.关键词
空海联合作战/深度强化学习/近似策略优化/智能决策Key words
air-sea joint operation/deep reinforcement learning/proximal policy optimization/intelligent decision分类
信息技术与安全科学引用本文复制引用
宋晓程,冯舒婷,李陟,贾政轩,周国进,叶东..基于深度强化学习的空海联合作战智能决策新方法[J].南京航空航天大学学报（英文版）,2023,40(1):25-36,12.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(Nos.62073102,62203145)and the China Postdoctoral Science Foundation(No.2022M710948). （Nos.62073102,62203145）
