A human mixed strategy approach to deep reinforcement learning Nguyen, Ngoc Duy, Nahavandi, Saeid and Nguyen, Thanh Thi 2018, A human mixed strategy approach to deep reinforcement learning, in SMC 2018 : The making of a human-centered cyber world : Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics, Institute of Electrical and Electronics Engineers, Piscataway, N.J., pp. 4023-4028, doi: 10.1109/SMC.2018.00682. Attached Files Name Description MIMEType Size Downloads Title A human mixed strategy approach to deep reinforcement learning Author(s) Nguyen, Ngoc Duy Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Nguyen, Thanh Thi orcid.org/0000-0001-9709-1663 Conference name IEEE Systems, Man, and Cybernetics Society. Conference (2018 : Miyazaki, Japan) Conference location Miyazaki, Japan Conference dates 2018/10/07 - 2018/10/10 Title of proceedings SMC 2018 : The making of a human-centered cyber world : Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics Editor(s) [Unknown] Publication date 2018 Series IEEE Systems, Man, and Cybernetics Society Conference Start page 4023 End page 4028 Total pages 6 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Keyword(s) Games Training Task analysis Simulation History Reinforcement learning MIMICS Science & Technology Technology Computer Science, Cybernetics Computer Science, Information Systems Computer Science ISBN 9781538666500 Language eng DOI 10.1109/SMC.2018.00682 HERDC Research category E1 Full written paper - refereed Copyright notice ©2018, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30122023 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 22 May 2019, 09:49:37 EST Wed, 22 May 2019, 09:50:48 EST Wed, 22 May 2019, 09:52:38 EST Thu, 23 May 2019, 05:00:14 EST Thu, 23 May 2019, 06:03:57 EST Fri, 24 May 2019, 08:53:26 EST Fri, 24 May 2019, 08:58:50 EST Fri, 24 May 2019, 08:59:39 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 148 Abstract Views, 6 File Downloads  -  Detailed Statistics Created: Wed, 22 May 2019, 09:52:38 EST

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