Multi-UAV collaborative pursuit method via hierarchical reinforcement learning
SUN Yi-hao
YAN Chao
XIANG Xiao-jia
TANG Deng-qing
ZHOU Han
JIANG Jie
Abstract：Aiming at the dynamic target pursuit problem in the complex obstacle environment,a multi-UAV collab-orative pursuit method via hierarchical reinforcement learning is proposed.This method contains two levels of learning process:the low-level sub-policy learning and the high-level sub-policy switching.Specifically,the collaborative pursuit task is decomposed into two sub-tasks,navigation obstacle avoidance and navigation collision avoidance.The correspond-ing sub-policies are learned independently to endow the UAV with skills of obstacle avoidance and collision avoidance required for collaborative pursuit.On this basis,a sparse reward function with a switching penalty is designed to train the high-level sub-policy switching module,which avoids the dependence on manually defined rules and realizes the automat-ic combination of underlying skills.Results of numerical simulation and software-in-the-loop experiments show that the proposed method can significantly reduce the learning difficulty of the pursuit policy,and has the highest success rate of pursuit compared with the baseline methods.
Keywords：hierarchical reinforcement learningobstacle avoidancecollision avoidancemulti-UAV pursuit
Publication Date：2025-01-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：13( 96-108 )