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Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management
Huang, Chao; Zhang, Hongcai; Wang, Long; Luo, Xiong; Song, Yonghua
2022-05
Source PublicationJournal of Modern Power Systems and Clean Energy
ISSN2196-5625
Volume10Issue:3Pages:743-754
KeywordDemand Response
DOI10.35833/MPCE.2021.000394
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000797467700020
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorHuang, Chao
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, Macao, 999078, Macao
2.School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
Recommended Citation
GB/T 7714
Huang, Chao,Zhang, Hongcai,Wang, Long,et al. Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management[J]. Journal of Modern Power Systems and Clean Energy,2022,10(3):743-754.
APA Huang, Chao,Zhang, Hongcai,Wang, Long,Luo, Xiong,&Song, Yonghua.(2022).Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management.Journal of Modern Power Systems and Clean Energy,10(3),743-754.
MLA Huang, Chao,et al."Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management".Journal of Modern Power Systems and Clean Energy 10.3(2022):743-754.
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