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Cooperative capture by multi-agent using reinforcement learning application for security patrol systems

机译:使用增强学习应用程序的多智能体进行协同捕获,用于安全巡逻系统

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Aim of this study is to create a security patrol system which is a cooperative capturing system by using multi-agent in the building. A host computer deploys autonomous robots as agents and find the best strategy to enclose an intruder. From the view point of the pursuit problem by multi-agent, reinforcement learning theory is one of choices to find way how to enclose an intruder. In order to apply reinforcement learning theory to security patrol systems, this study introduces how to discretize patrol areas. Some RFID tags are embedded in the floor and each autonomous robot can know the location where it is by sensing RFID tags, then sends locational information to the host computer. The host computer calculates positioning of the autonomous robots based on received locational data through wireless network. We make a prototype of patrol system and show how it works in this paper.
机译:这项研究的目的是通过在建筑物中使用多主体来创建一个安全巡逻系统,该系统是一个协作捕获系统。主机将自主机器人部署为代理,并找到围堵入侵者的最佳策略。从多主体的追赶问题的角度来看,强化学习理论是寻找如何围堵入侵者的一种选择。为了将强化学习理论应用于安全巡逻系统,本研究介绍了如何离散化巡逻区域。一些RFID标签嵌入在地板上,每个自动机器人可以通过感应RFID标签知道位置,然后将位置信息发送到主机。主机基于通过无线网络接收到的位置数据来计算自主机器人的位置。在本文中,我们制作了巡逻系统的原型,并演示了其工作原理。

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