Cooperatively pursuing a target unmanned aerial vehicle by multiple unmanned aerial vehicles based on multiagent reinforcement learning. Issue 2 (17th February 2020)

• Record Type: Journal Article
• Authors: Wang, Xiaoqiang; Xuan, Shuzhe; Ke, Liangjun
• Other Names: Duan Haibin guestEditor.; Fang Hao guestEditor.
• Is Part Of: Advanced control for applications. Volume 2:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-17
• Subjects: pursuit‐evasion -- reinforcement learning -- three‐dimensional environment -- unmanned aerial vehicle; Automatic control -- Periodicals; Automatic control; Periodicals; Electronic journals; 629.8
• DOI: 10.1002/adc2.27
• Languages: English
• ISSNs: 2578-0727

Abstract

Abstract: This article considers a pursuit‐evasion problem, in which multiple unmanned aerial vehicles (multi‐UAVs) are required to cooperatively pursue a moving target UAV. The dynamics of the three‐dimensional partially unknown environment makes it hard to deal with this problem. A multiagent reinforcement learning approach is proposed. This algorithm is extended from cooperative double Q‐learning by taking into account of the characteristics of the studied pursuit problem. To study the performance, a simulator is developed, which concerns the dynamic nature of the environment and flight constraints of UAV. Compared with several state‐of‐the‐art reinforcement learning algorithms, the proposed algorithm provides better policy such that the pursuing multi‐UAVs can effectively chase the moving target UAV. Abstract : This article considers a pursuit‐evasion problem, in which multiple unmanned aerial vehicles (multi‐UAVs) are required to cooperatively pursue a moving target UAV. In 3‐D continuous space, we use different types of obstacles to design a variety of chase scenes. According to the characteristics of the tracking problem, a multiagent reinforcement learning method is proposed. The experiment shows that the method can achieve good results.