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• W4294151808 startingPage "6392" @default.
• W4294151808 abstract "The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods." @default.
• W4294151808 created "2022-09-02" @default.
• W4294151808 creator A5032053457 @default.
• W4294151808 creator A5053663591 @default.
• W4294151808 date "2022-09-01" @default.
• W4294151808 modified "2023-10-04" @default.
• W4294151808 title "Reinforcement Learning: Theory and Applications in HEMS" @default.
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