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

A Web of Things Architecture for Digital Twin Creation and Model-Based Reinforcement Control