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Articles
Deep reinforcement learning and 3D physical environments applied to crowd evacuation in congested scenarios
Dong Zhanga National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China;b University of Chinese Academy of Sciences, Beijing, People’s Republic of China
https://orcid.org/0000-0003-2510-9814View further author information
Wenhang Lia National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaCorrespondence[email protected]
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Jianhua Gonga National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China;b University of Chinese Academy of Sciences, Beijing, People’s Republic of China;c Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, People’s Republic of ChinaView further author information
, Guoyong Zhanga National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
, Jiantao Liud School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, People’s Republic of ChinaView further author information
, Lin Huanga National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
, Heng Liua National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
& Haonan Maa National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
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Pages 691-714
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Received 18 Nov 2022, Accepted 15 Feb 2023, Published online: 02 Mar 2023
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