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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:

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 179

Showing 1-25 of 179 citing articles:

A Survey on Curriculum Learning
Xin Wang, Yudong Chen, Wenwu Zhu
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021), pp. 1-1
Open Access | Times Cited: 389
Deep reinforcement learning in smart manufacturing: A review and prospects
Chengxi Li, Pai Zheng, Yue Yin, et al.
CIRP journal of manufacturing science and technology (2022) Vol. 40, pp. 75-101
Closed Access | Times Cited: 148
Towards hybrid human‐AI learning technologies
Inge Molenaar
European Journal of Education (2022) Vol. 57, Iss. 4, pp. 632-645
Open Access | Times Cited: 82
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
Robert Kirk, Amy Zhang, Edward Grefenstette, et al.
Journal of Artificial Intelligence Research (2023) Vol. 76, pp. 201-264
Open Access | Times Cited: 71
Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
James Orr, Ayan Dutta
Sensors (2023) Vol. 23, Iss. 7, pp. 3625-3625
Open Access | Times Cited: 60
Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions
Robert X. Gao, Jörg Krüger, Marion Merklein, et al.
CIRP Annals (2024) Vol. 73, Iss. 2, pp. 723-749
Open Access | Times Cited: 14
An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction
Chengxi Li, Pai Zheng, Yue Yin, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 80, pp. 102471-102471
Closed Access | Times Cited: 62
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
Jack Parker-Holder, Raghu Rajan, Xingyou Song, et al.
Journal of Artificial Intelligence Research (2022) Vol. 74, pp. 517-568
Open Access | Times Cited: 53
Curriculum Learning for Goal-Oriented Semantic Communications With a Common Language
Mohammad Karimzadeh‐Farshbafan, Walid Saad, Mérouane Debbah
IEEE Transactions on Communications (2023) Vol. 71, Iss. 3, pp. 1430-1446
Open Access | Times Cited: 25
How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence
Xuemin Hu, Shen Li, Tingyu Huang, et al.
IEEE Transactions on Intelligent Vehicles (2023) Vol. 9, Iss. 1, pp. 593-612
Open Access | Times Cited: 25
Autonomous navigation of mobile robots in unknown environments using off-policy reinforcement learning with curriculum learning
Yan Yin, Zhiyu Chen, Gang Liu, et al.
Expert Systems with Applications (2024) Vol. 247, pp. 123202-123202
Closed Access | Times Cited: 13
A scoping review of reinforcement learning in education
Bahar Memarian, Tenzin Doleck
Computers and Education Open (2024) Vol. 6, pp. 100175-100175
Open Access | Times Cited: 7
Cooperative pursuit of unauthorized UAVs in urban airspace via Multi-agent reinforcement learning
Wenbo Du, Tong Guo, Jun Chen, et al.
Transportation Research Part C Emerging Technologies (2021) Vol. 128, pp. 103122-103122
Closed Access | Times Cited: 43
Deep Reinforcement Learning-Based Air-to-Air Combat Maneuver Generation in a Realistic Environment
Jung Ho Bae, Hoseong Jung, Seogbong Kim, et al.
IEEE Access (2023) Vol. 11, pp. 26427-26440
Open Access | Times Cited: 19
Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization
Lu Chang, Liang Shan, Weilong Zhang, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 83, pp. 102570-102570
Closed Access | Times Cited: 19
Application of reinforcement learning based on curriculum learning for the pipe auto-routing of ships
Youngsu Kim, Kyung‐Ho Lee, Byeong-Wook Nam, et al.
Journal of Computational Design and Engineering (2023) Vol. 10, Iss. 1, pp. 318-328
Open Access | Times Cited: 16
Deep Learning and Reinforcement Learning for Assessing and Enhancing Academic Performance in University Students: A Scoping Review
Fabrizio Stasolla, Antonio Zullo, Roberto Maniglio, et al.
AI (2025) Vol. 6, Iss. 2, pp. 40-40
Open Access
Outsmarting algorithms: A comparative battle between Reinforcement Learning and heuristics in Atari Tetris
Julius A. Bairaktaris, Arne Johannssen
Expert Systems with Applications (2025), pp. 127251-127251
Open Access
An efficient encoder-decoder network for the capacitated vehicle routing problem
Jia Luo, Chaofeng Li
Expert Systems with Applications (2025), pp. 127311-127311
Closed Access
Curriculum-Based Deep Reinforcement Learning for Quantum Control
Hailan Ma, Daoyi Dong, Steven X. Ding, et al.
IEEE Transactions on Neural Networks and Learning Systems (2022) Vol. 34, Iss. 11, pp. 8852-8865
Open Access | Times Cited: 26
Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework
Mohammad Karimzadeh‐Farshbafan, Walid Saad, Mérouane Debbah
ICC 2022 - IEEE International Conference on Communications (2022), pp. 1710-1715
Open Access | Times Cited: 25
Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery
Paul Maria Scheikl, Eleonora Tagliabue, Balázs Gyenes, et al.
IEEE Robotics and Automation Letters (2022) Vol. 8, Iss. 2, pp. 560-567
Open Access | Times Cited: 22
Semantics-Empowered Communications: A Tutorial-Cum-Survey
Zhilin Lu, Rongpeng Li, Kun Lü, et al.
IEEE Communications Surveys & Tutorials (2023) Vol. 26, Iss. 1, pp. 41-79
Closed Access | Times Cited: 13
Heterogeneous reinforcement learning for defending power grids against attacks
Mohammadamin Moradi, Shirin Panahi, Zheng-Meng Zhai, et al.
APL Machine Learning (2024) Vol. 2, Iss. 2
Open Access | Times Cited: 4
Towards robust shielded reinforcement learning through adaptive constraints and exploration: The fear field framework
Haritz Odriozola-Olalde, Maider Zamalloa, Nestor Arana-Arexolaleiba, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110055-110055
Closed Access
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