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

  • Tetsuya OdaEmail author
  • Chiaki Ueda
  • Ryo Ozaki
  • Kengo Katayama
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

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.

Notes

Acknowledgement

This work was supported by Faculty of Engineering, Okayama University of Science (OUS) Grant-in-Aid for Exploratory Research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tetsuya Oda
    • 1
    Email author
  • Chiaki Ueda
    • 1
  • Ryo Ozaki
    • 1
  • Kengo Katayama
    • 1
  1. 1.Department of Information and Computer EngineeringOkayama University of Science (OUS)OkayamaJapan

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