Review
Version 1
Preserved in Portico This version is not peer-reviewed
Reinforcement Learning: Theory and Applications in HEMS
Version 1
: Received: 3 August 2022 / Approved: 5 August 2022 / Online: 5 August 2022 (04:43:42 CEST)
Version 2 : Received: 31 August 2022 / Approved: 1 September 2022 / Online: 1 September 2022 (04:27:12 CEST)
Version 2 : Received: 31 August 2022 / Approved: 1 September 2022 / Online: 1 September 2022 (04:27:12 CEST)
A peer-reviewed article of this Preprint also exists.
Al-Ani, O.; Das, S. Reinforcement Learning: Theory and Applications in HEMS. Energies 2022, 15, 6392. Al-Ani, O.; Das, S. Reinforcement Learning: Theory and Applications in HEMS. Energies 2022, 15, 6392.
Abstract
The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. 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. The survey suggests that this research is still in its infancy.
Keywords
HEMS; Reinforcement Learning; Deep Neural Network; Q-Value; Policy Gradient; Natural Gradient; Actor-Critic; Residential, Commercial, Academic.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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