深度强化学习驱动的超视距空战自主决策方法OA

With the rapid development of airborne sensor technologies and medium-to-long-range air-to-air missile technologies,beyond visual range air combat has become the dominant form of modern air warfare.In such a com-plex and dynamic operational environment,the development of intelligent technologies capable of real-time battle-field situation awareness and rational maneuver decision-making has emerged as a research hotspot in the field of mi-litary technology.First,a high-fidelity simulation environment is constructed,encompassing a six-degree-of-freedom aircraft dynamics model,a missile guidance system model,and a radar sensor system.Subsequently,integrating im-itation learning and self-play methods,an opponent-learning-based air combat decision-making framework is pro-posed to address the poor adaptability and generalization of deep reinforcement learning in aerial combat,thereby en-hancing the agent's ability to rapidly adapt and optimize strategies in complex and variable battlefield environ-ments.Finally,ten expert systems with significant tactical differences are developed to engage in game-based confron-tations with the agent within the high-fidelity air combat simulation platform.The results demonstrate that the propo-sed decision-making framework significantly outperform traditional deep reinforcement learning strategies in key met-rics such as convergence speed and winning rate,exhibiting strong effectiveness and generalization.This work can pro-vide technical support for the rapid generation of reliable strategies in complex beyond visual range air combat scenarios.