Indoor Semantic Location Privacy Protection With Safe Reinforcement Learning-Reference-Cited by-同舟云学术

Indoor Semantic Location Privacy Protection With Safe Reinforcement Learning

Published:2023-10 Issue:5 Volume:9 Page:1385-1398
ISSN:2332-7731
Container-title:IEEE Transactions on Cognitive Communications and Networking
language:
Short-container-title:IEEE Trans. Cogn. Commun. Netw.

Author:

Min Minghui¹^ORCID,Yang Shuang¹^ORCID,Zhang Hongliang²^ORCID,Ding Jicun³^ORCID,Peng Guojun⁴^ORCID,Pan Miao⁵,Han Zhu⁵^ORCID

Affiliation:

1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

2. School of Electronics, Peking University, Beijing, China

3. Xuzhou First People’s Hospital, Xuzhou, China

4. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, and the School of Cyber Science and Engineering, Wuhan University, Wuhan, China

5. Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

China Postdoctoral Science Foundation

NSF

U.S. Department of Transportation, Toyota and Amazon

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture

Link

https://ieeexplore.ieee.org/ielam/6687307/10272990/10171230-aam.pdf

Reference43 articles.

1. Safe reinforcement learning via shielding;alshiekh;Proc 32nd AAAI Conf Artif Intell,2017

2. Double Q-learning;mendes;Proc Adv Neural Inf Process Syst (NIPS),2010

3. A review of safe reinforcement learning: Methods, theory and applications;gu;arXiv 2205 10330,2022

4. Statistical arbitrage trading on the intraday market using the asynchronous advantage actor–critic method

5. SEM: Safe exploration mask for q-learning

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