基于深度强化学习的无人机博弈路径规划

打开文本图片集

中图分类号： V279 文献标志码： A 文章编号： 1671-6841（2025）04-0008-07

DOI： 10. 13705 / j. issn. 1671-6841. 2024033

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 experience 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 improved the convergence speed and learning stability， and the trained agents demonstrated better autonomous game path planning capabilities.

Key words： deep reinforcement learning; UAV game; path planning; genetic algorithm

0 引言

近年来，无人机作为快速高效的作战平台，以其灵活多样的作战形式和安全可靠的使用特性，在高精度、快节奏的现代局部战争中发挥了巨大作用[ 1] 。（剩余11368字）