Internet of Things (IoT) devices are available in a multitude of scenarios, and provide constant, contextual data which can be leveraged to automatically reconfigure and optimize smart environments. To realize this vision, Artificial Intelligence (AI) and deep learning techniques are usually employed, however they need large quantity of data which is often not feasible in IoT scenarios. Digital Twins (DTs) have recently emerged as an effective way to replicate physical entities in the digital domain, to allow for simulation and testing of models and services. In this paper, we present a novel architecture based on the emerging Web of Things (WoT) standard, which provides a DT of a smart environment and applies Deep Reinforcement Learning (DRL) techniques on real time data. We implement our system in a real deployment, and test it along with a legacy system. Our findings show that the benefits of having a digital twin, specifically for DRL models, allow for faster convergence and finer tuning.

翻译：各种情况中都存在物的互联网(IoT)装置,它提供了可自动重新配置和优化智能环境的经常性背景数据。为了实现这一愿景,通常使用人工智能和深层学习技术,但它们需要大量数据,但在IoT情景中往往不可行。数字双胞胎(DTs)最近作为一种在数字领域复制物理实体的有效方式出现,以便模拟和测试模型和服务。在本文中,我们介绍了一个基于新兴的“物网(WoT)”标准的新颖结构,该标准提供了智能环境的脱钩,并将深强化学习技术应用于实时数据。我们实际应用了我们的系统,并与遗留系统一起测试。我们的调查结果显示,数字双胞胎(特别是DRL模型)的好处是能够更快地趋同和微调。