空天防御2024，Vol.7Issue(1)：40-47,8.
基于分层强化学习的低过载比拦截制导律
Intercept Guidance Law with a Low Acceleration Ratio Based on Hierarchical Reinforcement Learning
摘要
Abstract
This paper has proposed an intercept guidance law based on hierarchical reinforcement learning to solve the three-dimensional maneuvering target intercept guidance problem with constraints of low acceleration ratio and bearings-only measurement.The aforementioned problem was initially modelled using a Markov decision process model,where a heuristic reward function was applied considering both the energy consumption and the missile-to-target line of sight(LOS)angular rate.Besides,the policy of two levels was built up with the lower-level policy generating the required guidance command and being supervised by subgoals that were instructed by the higher levels,allowing the convergence of the LOS angular rate and guaranteeing the successful interception against a maneuvering target.Simulation results have validated the superiority of the proposed method compared with the augmented proportional navigation guidance law in terms of intercept accuracy and hit probability,and its required acceleration ratio is much lower.关键词
末制导/机动目标拦截/低过载比/分层强化学习Key words
guidance law/maneuvering target intercept/low acceleration ratio/hierarchical reinforcement learning分类
军事科技引用本文复制引用
王旭,蔡远利,张学成,张荣良,韩成龙..基于分层强化学习的低过载比拦截制导律[J].空天防御,2024,7(1):40-47,8.基金项目
国家自然科学基金项目(62203349,12302061) （62203349,12302061）
