空天防御2025，Vol.8Issue(3)：50-58,85,10.
基于博弈树与数字平行战场的空战决策方法
Air Combat Decision-Making Method Based on Game Tree and Digital Parallel Simulation Battlefield
摘要
Abstract
In air combat decision-making,effectively identifying the key states and improving the decision-making ability of intelligent bodies in these states is the key research direction of reinforcement learning algorithms.In this paper,a dynamic strategy switching framework built by deep reinforcement learning was proposed for the intelligent body problem in air combat decision-making,aiming at increasing the decision-making quality of the intelligent body in the complex environment.This study identified critical states using dimensionality reduction and classification of high-dimensional state space through representation learning and cluster analysis techniques in the non-critical state,a deep reinforcement learning algorithm was employed for decision-making;in the critical state,an inverse dynamics model was adopted to generates the target state's corresponding action sequence and a parallel simulation strategy was utilized to execute the action sequence in multiple simulation environments to approximate the target state rapidly.At the end of the simulation,the optimal decision path was determined by advantage value evaluation.The experimental results show that the method can improve the decision-making ability of the intelligent body in critical states,providing a new solution for intelligent decision-making in complex air combat environments.关键词
空战决策/深度强化学习/关键状态识别/平行仿真/优势值评估Key words
air combat decision-making/deep reinforcement learning/critical state identification/parallel simulation/advantage value evaluation分类
军事科技引用本文复制引用
周文杰,付昱龙,郭相科,戚玉涛,张海宾..基于博弈树与数字平行战场的空战决策方法[J].空天防御,2025,8(3):50-58,85,10.基金项目
陕西省自然科学基础研究计划项目(2023-JC-YB-529) （2023-JC-YB-529）
