标题： 一种在工业环境中部署强化学习的架构 显示英文标题

标题： An Architecture for Deploying Reinforcement Learning in Industrial Environments

摘要： 工业4.0由缩短上市时间、产品大规模定制和单件生产等需求推动。强化学习（RL），一种被证明在众多复杂任务中能够提高并超越人类水平表现的机器学习范式，有助于应对上述需求。在本文中，我们提出了一种基于OPC UA的操作技术（OT）感知的强化学习架构，该架构扩展了标准的强化学习设置，并将其与数字孪生的设置相结合。此外，我们定义了一个OPC UA信息模型，允许使用强化学习代理进行通用的即插即用式方法交换。最后，我们通过创建一个概念验证来展示和评估该架构。通过解决一个玩具示例，我们表明该架构可以用于确定使用实际控制系统的最佳策略。 显示英文摘要

摘要： Industry 4.0 is driven by demands like shorter time-to-market, mass customization of products, and batch size one production. Reinforcement Learning (RL), a machine learning paradigm shown to possess a great potential in improving and surpassing human level performance in numerous complex tasks, allows coping with the mentioned demands. In this paper, we present an OPC UA based Operational Technology (OT)-aware RL architecture, which extends the standard RL setting, combining it with the setting of digital twins. Moreover, we define an OPC UA information model allowing for a generalized plug-and-play like approach for exchanging the RL agent used. In conclusion, we demonstrate and evaluate the architecture, by creating a proof of concept. By means of solving a toy example, we show that this architecture can be used to determine the optimal policy using a real control system.