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Deep Q-Learning for Home Automation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 152 - Number 6 |
| Year of Publication: 2016 |
| Authors: Vignesh Gokul, Parinitha Kannan, Sharath Kumar, Shomona Gracia Jacob |
10.5120/ijca2016911873
|
Vignesh Gokul, Parinitha Kannan, Sharath Kumar, Shomona Gracia Jacob . Deep Q-Learning for Home Automation. International Journal of Computer Applications. 152, 6 ( Oct 2016), 1-5. DOI=10.5120/ijca2016911873
Abstract
In this paper, the first deep reinforcement learning model for home automation systems is presented. Home automation has been one of the most important applications in the field of Artificial Intelligence. The system should learn the pattern and behaviour of the user automatically from experience and take future actions accordingly. The system proposed here makes use only of images to learn the user’s needs using Deep Q-Learning, thus minimizing the use of any sensors and other hardware. The model makes use of a Convolutional Neural Network that takes as input, the image and outputs the future reward for each action. The system was tested with images of a house and describes the methods and results in the paper.
References
- Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 2012.
- Natalie Kcomt Ch´e, Niels Pardons, Yves Vanrompay, Davy Preuveneers, and Yolande Berbers. An intelligent domotics system to automate user actions. In Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAmI 2010), pages 201–204. Springer, 2010.
- Sajal K Das and Diane J Cook. Designing smart environments: A paradigm based on learning and prediction. In International Conference on Pattern Recognition and Machine Intelligence, pages 80–90. Springer, 2005.
- Peter Gorniak and David Poole. Predicting future user actions by observing unmodified applications. In AAAI/IAAI, pages 217–222, 2000.
- Edwin O Heierman and Diane J Cook. Improving home automation by discovering regularly occurring device usage patterns. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pages 537–540. IEEE, 2003.
- Li Jiang, Da-You Liu, and Bo Yang. Smart home research. In Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, volume 2, pages 659–663. IEEE, 2004.
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
- Michael C Mozer. The neural network house: An environment hat adapts to its inhabitants. In Proc. AAAI Spring Symp. Intelligent Environments, volume 58, 1998.
- Mamun Bin Ibne Reaz, Awss Assim, Muhammad I Ibrahimy, Florence Choong, and Faisal Mohd-Yasin. Hardware simulation of home automation using pattern matching and reinforcement learning for disabled people. In IC-AI, pages 213– 218, 2008.
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