期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 34, 期 10, 页码 7900-7909出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3146976
关键词
Games; Reinforcement learning; Physics; Engines; Urban areas; Real-time systems; Trajectory; Multiagent reinforcement learning; multiquadcopter motion planning; pursuit-evasion game; trajectory prediction
In this article, the application of multiquadcopter and target pursuit-evasion game in obstacles environment is investigated. A pursuit-evasion scenario framework is proposed for high-quality simulation of urban environment, and a multiagent coronal bidirectionally coordinated network with target prediction is constructed to ensure the effectiveness of the damaged "swarm" system in pursuit-evasion mission.
As one of the tiniest flying objects, unmanned aerial vehicles (UAVs) are often expanded as the ``swarm'' to execute missions. In this article, we investigate the multiquadcopter and target pursuit-evasion game in the obstacles environment. For high-quality simulation of the urban environment, we propose the pursuit-evasion scenario (PES) framework to create the environment with a physics engine, which enables quadcopter agents to take actions and interact with the environment. On this basis, we construct multiagent coronal bidirectionally coordinated with target prediction network (CBC-TP Net) with a vectorized extension of multiagent deep deterministic policy gradient (MADDPG) formulation to ensure the effectiveness of the damaged ``swarm'' system in pursuit-evasion mission. Unlike traditional reinforcement learning, we design a target prediction network (TP Net) innovatively in the common framework to imitate the way of human thinking: situation prediction is always before decision-making. The experiments of the pursuit-evasion game are conducted to verify the state-of-the-art performance of the proposed strategy, both in the normal and antidamaged situations.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据