A Penetration Method for UAV Based on Distributed Reinforcement Learning and Demonstrations

Kexv Li, Yue Wang*, Xing Zhuang, Hao Yin, Xinyu Liu, Hanyu Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in modern warfare. Enhancing UAV’s ability of autonomous penetration through machine learning has become a research hotspot. However, the current generation of autonomous penetration strategies for UAVs faces the problem of excessive sample demand. To reduce the sample demand, this paper proposes a combination policy learning (CPL) algorithm that combines distributed reinforcement learning and demonstrations. Innovatively, the action of the CPL algorithm is jointly determined by the initial policy obtained from demonstrations and the target policy in the asynchronous advantage actor-critic network, thus retaining the guiding role of demonstrations in the initial training. In a complex and unknown dynamic environment, 1000 training experiments and 500 test experiments were conducted for the CPL algorithm and related baseline algorithms. The results show that the CPL algorithm has the smallest sample demand, the highest convergence efficiency, and the highest success rate of penetration among all the algorithms, and has strong robustness in dynamic environments.

Original languageEnglish
Article number232
JournalDrones
Volume7
Issue number4
DOIs
Publication statusPublished - Apr 2023

Keywords

  • UAV penetration
  • asynchronous advantage actor-critic
  • demonstrations
  • distributed reinforcement learning

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