计算机科学
人工智能
深度学习
强化学习
模仿
建筑
机器学习
人工神经网络
控制(管理)
深层神经网络
国家(计算机科学)
算法
心理学
社会心理学
艺术
视觉艺术
作者
Omar Al-Ani,Sanjoy Das,Hongyu Wu,Dania Martinez-Figueora,Xuebo Liu,Rahul Harsha Cheppally
标识
DOI:10.1109/ssci51031.2022.10022217
摘要
A deep neural network model is proposed in this research for indoor environmental prediction and control in the smart home. It attempts to benefit directly from human experience by making use of imitation learning, a paradigm that is closely related to reinforcement learning. In imitation learning, the agent learns from real human experience. The research uses the state-of-the-art deep attentive tabular network architecture, which is an extension of deep neural networks. The tabular network, which is designed specifically to handle tabular data, is able to outperform all other machine learning algorithms in current use. The proposed model incorporates four such tabular networks. Promising results described here demonstrate how current developments in machine learning can be adopted effectively in HEMS related applications.
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