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This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems.
Reinforcement Learning: An Introduction
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D. Crawley, L. Lawrie, F. Winkelmann, and C. Pedersen · 2001
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Simple and scalable predictive uncertainty estimation using deep ensembles
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Td or not td: Analyzing the role of temporal differencing in deep reinforcement learning
A. Amiranashvili, A. Dosovitskiy, V. Koltun, and T. Brox · 2018
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
J. Buckman, D. Hafner, G. Tucker, E. Brevdo, and H. Lee · 2018
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The Future of Cooling
IEA · 2018
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G. Dulac-Arnold, D. J. Mankowitz, and T. Hester · 2019
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S. Levine, A. Kumar, G. Tucker, and J. Fu · 2020
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Regularized behavior value estimation
C. Gulcehre, S. G. Colmenarejo, Z. Wang, J. Sygnowski, T. Paine, K. Zolna, Y. Chen, M. Hoffman, R. Pascanu, and N. de Freitas · 2021
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A review of deep reinforcement learning for smart building energy management
L. Yu, S. Qin, M. Zhang, C. Shen, T. Jiang, and X. Guan · 2021
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Semi-analytical industrial cooling system model for reinforcement learning, 2022
Y. Chervonyi, P. Dutta, P. Trochim, O. Voicu, C. Paduraru, C. Qian, E. Karagozler, J. Q. Davis, R. Chippendale, G. Bajaj, S. Witherspoon, and J. Luo · 2022
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ANSI/ASHRAE Standard 55-2020. Thermal Environmental Conditions for Human Occupancy, 2020
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