Design of a Deep Q-Network Based Simulation System for Actuation Decision in Ambient Intelligence

Abstract

Ambient Intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. This paper presents design and implementation of a simulation system based on Deep Q-Network (DQN) for actuation decision in AmI. DQN is a deep neural network structure used for estimation of Q-value of the Q-learning method. We implemented the proposed simulating system by Rust programming language. We describe the design and implementation of the simulation system, and show some simulation results to evaluate its performance.