航空兵器2026，Vol.33Issue(2)：44-53,10.DOI:10.12132/ISSN.1673-5048.2025.0145
面向大机动目标的高效强化学习拦截制导律研究
Research on Efficient Reinforcement Learning Interception Guidance Law for Large Maneuvering Targets
摘要
Abstract
In response to the challenges posed by intercepting highly maneuverable targets,including difficulties in obtaining target acceleration information,insufficient adaptability of traditional guidance laws to high-dynamic environments,low exploration efficiency of reinforcement learning algo-rithms in wide spatial-temporal contexts,and poor training stability,this paper proposes a terminal guidance law based on a double random distillation network and real proximal policy optimization algo-rithm.Firstly,a Markov decision process is designed for the three-dimensional terminal guidance sce-nario.Subsequently,deep reinforcement learning algorithms are employed to train high-speed vehicles.To enhance the exploration efficiency of these vehicles within wide spatial-temporal con-texts,a double random distillation network is introduced that incentivizes the exploration of unknown states through internal rewards.To accelerate training speed and ensure training stability,the trust region rollback objective function of real proximal policy optimization algorithm is used to replace the clipping objective function for effectively improving the convergence rate while enhancing training sta-bility.Simulation results demonstrate that the proposed terminal guidance law exhibits generalization and robustness when faced with highly maneuverable targets,and can achieve efficient interception without requiring target acceleration information.关键词
末制导律/强化学习/训练效率/近端策略优化/随机网络蒸馏/飞行器Key words
terminal guidance law/reinforcement learning/training efficiency/proximal policy optimization/random distillation network/vehicle分类
军事科技引用本文复制引用
张烨,涂远刚,郭正玉,王靖宇..面向大机动目标的高效强化学习拦截制导律研究[J].航空兵器,2026,33(2):44-53,10.基金项目
国家自然科学基金项目(02564897) （02564897）
