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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:

An Introduction to Deep Reinforcement Learning
Vincent François-Lavet, Peter Henderson, Riashat Islam, et al.
Foundations and Trends® in Machine Learning (2018) Vol. 11, Iss. 3-4, pp. 219-354
Open Access | Times Cited: 897

Showing 1-25 of 897 citing articles:

Review of Deep Learning Algorithms and Architectures
Ajay Shrestha, Ausif Mahmood
IEEE Access (2019) Vol. 7, pp. 53040-53065
Open Access | Times Cited: 1468
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
Deep learning enabled inverse design in nanophotonics
Sunae So, Trevon Badloe, Jaebum Noh, et al.
Nanophotonics (2020) Vol. 9, Iss. 5, pp. 1041-1057
Open Access | Times Cited: 422
Application of deep reinforcement learning to intrusion detection for supervised problems
Manuel López-Martín, Belén Carro, Antonio Sánchez-Esguevillas
Expert Systems with Applications (2019) Vol. 141, pp. 112963-112963
Open Access | Times Cited: 277
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Lei Lei, Yue Tan, Kan Zheng, et al.
IEEE Communications Surveys & Tutorials (2020) Vol. 22, Iss. 3, pp. 1722-1760
Open Access | Times Cited: 237
Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks
Le Liang, Hao Ye, Guanding Yu, et al.
Proceedings of the IEEE (2019) Vol. 108, Iss. 2, pp. 341-356
Open Access | Times Cited: 234
Drone Deep Reinforcement Learning: A Review
Ahmad Taher Azar, Anis Koubâa, Nada Ali Mohamed, et al.
Electronics (2021) Vol. 10, Iss. 9, pp. 999-999
Open Access | Times Cited: 216
Data mining and machine learning methods for sustainable smart cities traffic classification: A survey
Muhammad Shafiq, Zhihong Tian, Ali Kashif Bashir, et al.
Sustainable Cities and Society (2020) Vol. 60, pp. 102177-102177
Closed Access | Times Cited: 202
A review on deep reinforcement learning for fluid mechanics
Paul Garnier, Jonathan Viquerat, Jean Rabault, et al.
Computers & Fluids (2021) Vol. 225, pp. 104973-104973
Open Access | Times Cited: 199
QTune
Guoliang Li, Xuanhe Zhou, Shifu Li, et al.
Proceedings of the VLDB Endowment (2019) Vol. 12, Iss. 12, pp. 2118-2130
Closed Access | Times Cited: 185
Machine learning in aerodynamic shape optimization
Jichao Li, Xiaosong Du, Joaquim R. R. A. Martins
Progress in Aerospace Sciences (2022) Vol. 134, pp. 100849-100849
Open Access | Times Cited: 182
AI Models for Green Communications Towards 6G
Bomin Mao, Fengxiao Tang, Yuichi Kawamoto, et al.
IEEE Communications Surveys & Tutorials (2021) Vol. 24, Iss. 1, pp. 210-247
Open Access | Times Cited: 173
Machine Learning Methods for Small Data Challenges in Molecular Science
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, et al.
Chemical Reviews (2023) Vol. 123, Iss. 13, pp. 8736-8780
Open Access | Times Cited: 170
Combustion machine learning: Principles, progress and prospects
Matthias Ihme, Wai Tong Chung, Aashwin Mishra
Progress in Energy and Combustion Science (2022) Vol. 91, pp. 101010-101010
Open Access | Times Cited: 162
Direct shape optimization through deep reinforcement learning
Jonathan Viquerat, Jean Rabault, Alexander Kuhnle, et al.
Journal of Computational Physics (2020) Vol. 428, pp. 110080-110080
Open Access | Times Cited: 161
An introduction to deep learning in medical physics: advantages, potential, and challenges
Chenyang Shen, Dan Nguyen, Zhiguo Zhou, et al.
Physics in Medicine and Biology (2020) Vol. 65, Iss. 5, pp. 05TR01-05TR01
Open Access | Times Cited: 160
Deep reinforcement learning in medical imaging: A literature review
S. Kevin Zhou, Ngan Le, Khoa Luu, et al.
Medical Image Analysis (2021) Vol. 73, pp. 102193-102193
Open Access | Times Cited: 149
Automating turbulence modelling by multi-agent reinforcement learning
Guido Novati, Hugues Lascombes de Laroussilhe, Petros Koumoutsakos
Nature Machine Intelligence (2021) Vol. 3, Iss. 1, pp. 87-96
Closed Access | Times Cited: 147
News recommender system: a review of recent progress, challenges, and opportunities
Shaina Raza, Chen Ding
Artificial Intelligence Review (2021) Vol. 55, Iss. 1, pp. 749-800
Open Access | Times Cited: 134
Toward the third generation artificial intelligence
Bo Zhang, Jun Zhu, Hang Su
Science China Information Sciences (2023) Vol. 66, Iss. 2
Closed Access | Times Cited: 116
Deep reinforcement learning for inventory control: A roadmap
Robert Boute, Joren Gijsbrechts, Willem van Jaarsveld, et al.
European Journal of Operational Research (2021) Vol. 298, Iss. 2, pp. 401-412
Open Access | Times Cited: 114
Quantum machine learning for chemistry and physics
Manas Sajjan, Junxu Li, Raja Selvarajan, et al.
Chemical Society Reviews (2022) Vol. 51, Iss. 15, pp. 6475-6573
Open Access | Times Cited: 90
The Computational Theory of Mind
Matteo Colombo, Gualtiero Piccinini
(2023)
Closed Access | Times Cited: 75
Recent advances in reinforcement learning in finance
Ben Hambly, Renyuan Xu, Huining Yang
Mathematical Finance (2023) Vol. 33, Iss. 3, pp. 437-503
Open Access | Times Cited: 74
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