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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep Reinforcement Learning: An Overview
Yuxi Li
arXiv (Cornell University) (2017)
Open Access | Times Cited: 485

Showing 1-25 of 485 citing articles:

Deep Reinforcement Learning: A Brief Survey
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, et al.
IEEE Signal Processing Magazine (2017) Vol. 34, Iss. 6, pp. 26-38
Open Access | Times Cited: 3267
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, et al.
IEEE Communications Surveys & Tutorials (2019) Vol. 21, Iss. 4, pp. 3133-3174
Open Access | Times Cited: 1592
Deep Learning in Mobile and Wireless Networking: A Survey
Chaoyun Zhang, Paul Patras, Hamed Haddadi
IEEE Communications Surveys & Tutorials (2019) Vol. 21, Iss. 3, pp. 2224-2287
Open Access | Times Cited: 1498
A Survey on Deep Learning
Samira Pouyanfar, Saad Sadiq, Yilin Yan, et al.
ACM Computing Surveys (2018) Vol. 51, Iss. 5, pp. 1-36
Closed Access | Times Cited: 1159
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
IEEE Transactions on Cybernetics (2020) Vol. 50, Iss. 9, pp. 3826-3839
Open Access | Times Cited: 898
Applications of Deep Learning and Reinforcement Learning to Biological Data
Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, et al.
IEEE Transactions on Neural Networks and Learning Systems (2018) Vol. 29, Iss. 6, pp. 2063-2079
Open Access | Times Cited: 777
A brief survey of deep reinforcement learning
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, et al.
arXiv (Cornell University) (2017)
Open Access | Times Cited: 771
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges
Junfeng Xie, F. Richard Yu, Tao Huang, et al.
IEEE Communications Surveys & Tutorials (2018) Vol. 21, Iss. 1, pp. 393-430
Closed Access | Times Cited: 589
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?
Alessio Zappone, Marco Di Renzo, Mérouane Debbah
IEEE Transactions on Communications (2019) Vol. 67, Iss. 10, pp. 7331-7376
Open Access | Times Cited: 568
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Yaohua Sun, Mugen Peng, Yangcheng Zhou, et al.
IEEE Communications Surveys & Tutorials (2019) Vol. 21, Iss. 4, pp. 3072-3108
Open Access | Times Cited: 534
A Survey of Deep Learning Applications to Autonomous Vehicle Control
Sampo Kuutti, Richard Bowden, Yaochu Jin, et al.
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 22, Iss. 2, pp. 712-733
Open Access | Times Cited: 529
A survey and critique of multiagent deep reinforcement learning
Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor
Autonomous Agents and Multi-Agent Systems (2019) Vol. 33, Iss. 6, pp. 750-797
Closed Access | Times Cited: 494
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
Ioannis Antonopoulos, Valentin Robu, Benoit Couraud, et al.
Renewable and Sustainable Energy Reviews (2020) Vol. 130, pp. 109899-109899
Open Access | Times Cited: 442
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
Oshri Naparstek, Kobi Cohen
IEEE Transactions on Wireless Communications (2018) Vol. 18, Iss. 1, pp. 310-323
Open Access | Times Cited: 410
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP
Abdelhak Bentaleb, Bayan Taani, Ali C. Begen, et al.
IEEE Communications Surveys & Tutorials (2018) Vol. 21, Iss. 1, pp. 562-585
Open Access | Times Cited: 395
Reinforcement learning for control: Performance, stability, and deep approximators
Lucian Buşoniu, Tim de Bruin, Domagoj Tolić, et al.
Annual Reviews in Control (2018) Vol. 46, pp. 8-28
Open Access | Times Cited: 372
Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning
Shu Luo
Applied Soft Computing (2020) Vol. 91, pp. 106208-106208
Closed Access | Times Cited: 369
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G
Mojtaba Vaezi, Amin Azari, Saeed R. Khosravirad, et al.
IEEE Communications Surveys & Tutorials (2022) Vol. 24, Iss. 2, pp. 1117-1174
Closed Access | Times Cited: 345
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
Meixin Zhu, Yinhai Wang, Ziyuan Pu, et al.
Transportation Research Part C Emerging Technologies (2020) Vol. 117, pp. 102662-102662
Open Access | Times Cited: 328
Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing
Liang Wang, Kezhi Wang, Cunhua Pan, et al.
IEEE Transactions on Cognitive Communications and Networking (2020) Vol. 7, Iss. 1, pp. 73-84
Open Access | Times Cited: 317
Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
Dennis M. Dimiduk, Elizabeth A. Holm, Stephen R. Niezgoda
Integrating materials and manufacturing innovation (2018) Vol. 7, Iss. 3, pp. 157-172
Open Access | Times Cited: 287
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.
Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, et al.
arXiv (Cornell University) (2018)
Closed Access | Times Cited: 285
A Review of Deep Reinforcement Learning for Smart Building Energy Management
Liang Yu, Shuqi Qin, Meng Zhang, et al.
IEEE Internet of Things Journal (2021) Vol. 8, Iss. 15, pp. 12046-12063
Open Access | Times Cited: 251
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Kaiqing Zhang, Zhuoran Yang, Han Liu, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 246
Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments
Chao Yan, Xiaojia Xiang, Chang Wang
Journal of Intelligent & Robotic Systems (2019) Vol. 98, Iss. 2, pp. 297-309
Closed Access | Times Cited: 240
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