郑州大学学报（理学版）2025，Vol.57Issue(4)：8-14,7.DOI:10.13705/j.issn.1671-6841.2024033
基于深度强化学习的无人机博弈路径规划
UAV Game Path Planning Based on Deep Reinforcement Learning
摘要
Abstract
A deep reinforcement learning model driven by knowledge and data was proposed to address the low learning efficiency of deep reinforcement learning methods in complex environments for unmanned aerial vehicle(UAV)game tasks.Firstly,drawing on the idea of imitation learning,a genetic algorithm was employed as a heuristic search strategy,and expert experience knowledge was collected.Secondly,the UAV interacted with the environment through deep reinforcement learning and collected online experi-ence data.Finally,a deep reinforcement learning model driven by knowledge and data was constructed to optimize UAV game strategies.Experimental results indicated that the proposed model effectively im-proved the convergence speed and learning stability,and the trained agents demonstrated better autono-mous game path planning capabilities.关键词
深度强化学习/无人机博弈/路径规划/遗传算法Key words
deep reinforcement learning/UAV game/path planning/genetic algorithm引用本文复制引用
薛均晓,张世文,陆亚飞,严笑然,付玮..基于深度强化学习的无人机博弈路径规划[J].郑州大学学报（理学版）,2025,57(4):8-14,7.基金项目
国家重点研发计划项目(2022YFC3004400) （2022YFC3004400）
