System design perspective for human-level agents using deep reinforcement learning: a survey

Nguyen, Ngoc Duy, Nguyen, Thanh Thi and Nahavandi, Saeid 2017, System design perspective for human-level agents using deep reinforcement learning: a survey, IEEE Access, vol. 5, pp. 27091-27102, doi: 10.1109/ACCESS.2017.2777827.

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Title System design perspective for human-level agents using deep reinforcement learning: a survey
Author(s) Nguyen, Ngoc DuyORCID iD for Nguyen, Ngoc Duy orcid.org/0000-0001-9709-1663
Nguyen, Thanh ThiORCID iD for Nguyen, Thanh Thi orcid.org/0000-0002-0360-5270
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name IEEE Access
Volume number 5
Start page 27091
End page 27102
Total pages 12
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017-11-24
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Deep learning
human-level agents
reinforcement learning
robotics
survey
system design
NEURAL-NETWORKS
ALGORITHMS
MDPS
Language eng
DOI 10.1109/ACCESS.2017.2777827
Field of Research 080105 Expert Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2017, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30105415

Document type: Journal Article
Collection: Institute for Technology Research and Innovation
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