Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation

Florian Jaensch, A. Csiszar, Annika Kienzlen, A. Verl
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引用次数: 15

Abstract

In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy.
基于硬件在环仿真的物料流控制逻辑强化学习
本文提出了强化学习智能体的概念,该智能体可以通过作用于对象的数字孪生体(HiL仿真)来推断出对象的正确控制策略。这样,代理就代替了一个真正的控制系统。通过使用强化学习方法,提出了一个简单的物料流系统的概念验证应用程序,具有与PLC控制器硬件相同的数字孪生访问类型。利用所提出的方法,智能体能够找到正确的控制策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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