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      An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

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          A summary of demand response in electricity markets

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            A short-term building cooling load prediction method using deep learning algorithms

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              Is Open Access

              Asynchronous Methods for Deep Reinforcement Learning

              We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.
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                Author and article information

                Contributors
                Journal
                IEEE Transactions on Smart Grid
                IEEE Trans. Smart Grid
                Institute of Electrical and Electronics Engineers (IEEE)
                1949-3053
                1949-3061
                January 2021
                January 2021
                : 12
                : 1
                : 420-431
                Article
                10.1109/TSG.2020.3014055
                7a7c90b0-6257-4216-a616-82048d9ae49d
                © 2021

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

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