Deep reinforcement learning based strategy for quadrotor uav pursuer and evader problem

Dawei Chen, Yifei Wei, Li Wang, Choong Seon Hong, Li Chun Wang, Zhu Han

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In recent years, there have occurred many incidents that unmanned aerial vehicles (UAVs) in the field of national security. While in some situations, UAVs may be deployed simultaneously by different parties with opposite purposes, easily resulting in direct competitions against each other. In this case, how to use UAVs to pursue UAVs has become a hot spot. In order to analyze the behavior of UAV, building a realistic mathematical dynamic model is necessary. In this paper, we propose a Takagi-Sugeno (T-S) fuzzy control system based UAV dynamic model, which is exactly the same UAV control method in practice. To address the competition conundrum between UAVs, we formulate this problem into a pursuer-evader problem and leverage the reinforcement learning based machine learning method to solve this. The proposed deep Q network is based on traditional Q learning but able to address some deficiencies. Basically, deep Q network has three vital improvements: using neural network to describe the Q function, the architecture of double networks, and the experience replay. The simulation results show the correctness of our analysis and effectiveness of our proposed method.

原文English
主出版物標題2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728174402
DOIs
出版狀態Published - 六月 2020
事件2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Dublin, Ireland
持續時間: 7 六月 202011 六月 2020

出版系列

名字2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings

Conference

Conference2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
國家/地區Ireland
城市Dublin
期間7/06/2011/06/20

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