基于作战过程的岛礁兵力配置强化学习算法  

Reinforcement Learning Algorithm for Forces Allocation on Islands and Reefs Based on Combat Process

作　　者：肖凡 乔勇军 Xiao Fan;Qiao Yongjun(School of Coast Guard,Naval Aviation University,Yantai 264001,China)

机构地区：[1]海军航空大学岸防兵学院,山东烟台264001

出　　处：《兵工自动化》2022年第5期39-47,共9页Ordnance Industry Automation

摘　　要：针对岛礁守备作战过程中涉及的对海、对陆、对空3类武器,根据岛礁守备作战过程建立模型,提出一种动态动作空间方法。设置敌方武器装备、预设阵地、防守要地3类影响因素,利用不同的基于值函数的强化学习算法进行测试,通过测试能得到各武器装备最佳位置并判断预设阵地是否合理,通过比较可看出算法间各有优劣,适合的环境各不相同。结果表明:该方法能够运用于不同的环境,减少时空开销,提高岛礁守备决策的效率,有助于策略改进。Aiming at 3 kinds of weapons involved in island and reef garrison combat process, namely sea weapons, land weapons and air weapons, a model is established according to the island and reef garrison combat process, and a method of dynamic action space is proposed. 3 kinds of influencing factors are set, including enemy weapons and equipment, preset positions, and defensive points, and different reinforcement learning algorithms based on value function are used for testing.Through the test, the best position of each weapon and equipment can be obtained and whether the preset position is reasonable or not can be judged, and the comparison shows that the algorithms have their own advantages and disadvantages, and the suitable environments are different. The results show that the method can be applied to different environments, reduce the time and space overhead, improve the efficiency of island and reef garrison decision-making, and help to improve the strategy.

关 键 词：强化学习 值函数 岛礁守备 动态动作空间 

分 类 号：TJ01[兵器科学与技术—兵器发射理论与技术]