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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:

Reinforcement learning improves behaviour from evaluative feedback
Michael L. Littman
Nature (2015) Vol. 521, Iss. 7553, pp. 445-451
Closed Access | Times Cited: 342

Showing 1-25 of 342 citing articles:

Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Andreas Holzinger
Brain Informatics (2016) Vol. 3, Iss. 2, pp. 119-131
Open Access | Times Cited: 775
Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 741
Deep Reinforcement Learning: An Overview
Yuxi Li
arXiv (Cornell University) (2017)
Open Access | Times Cited: 485
Adaptive Dynamic Programming for Control: A Survey and Recent Advances
Derong Liu, Shan Xue, Bo Zhao, et al.
IEEE Transactions on Systems Man and Cybernetics Systems (2020) Vol. 51, Iss. 1, pp. 142-160
Closed Access | Times Cited: 462
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
Yansong Tang, Yi Tian, Jiwen Lu, et al.
(2018), pp. 5323-5332
Closed Access | Times Cited: 445
Runtime Neural Pruning
Ji Lin, Yongming Rao, Jiwen Lu, et al.
Neural Information Processing Systems (2017) Vol. 30, pp. 2181-2191
Closed Access | Times Cited: 383
A comprehensive survey on model compression and acceleration
Tejalal Choudhary, Vipul Kumar Mishra, Anurag Goswami, et al.
Artificial Intelligence Review (2020) Vol. 53, Iss. 7, pp. 5113-5155
Closed Access | Times Cited: 382
A Tutorial on Thompson Sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, et al.
(2018)
Open Access | Times Cited: 373
Society-in-the-loop: programming the algorithmic social contract
Iyad Rahwan
Ethics and Information Technology (2017) Vol. 20, Iss. 1, pp. 5-14
Closed Access | Times Cited: 370
A smart agriculture IoT system based on deep reinforcement learning
Fanyu Bu, Xin Wang
Future Generation Computer Systems (2019) Vol. 99, pp. 500-507
Closed Access | Times Cited: 290
From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies
Dan Wang, Chen Dong, Bin Song, et al.
IEEE Communications Magazine (2018) Vol. 56, Iss. 10, pp. 114-120
Closed Access | Times Cited: 278
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
Lefeng Cheng, Tao Yu
International Journal of Energy Research (2019) Vol. 43, Iss. 6, pp. 1928-1973
Open Access | Times Cited: 276
Control of synaptic plasticity in deep cortical networks
Pieter R. Roelfsema, Anthony Holtmaat
Nature reviews. Neuroscience (2018) Vol. 19, Iss. 3, pp. 166-180
Closed Access | Times Cited: 257
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Joel Z. Leibo, Vinícius Zambaldi, Marc Lanctot, et al.
arXiv (Cornell University) (2017)
Open Access | Times Cited: 251
A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
Felipe Leno da Silva, Anna Helena Reali Costa
Journal of Artificial Intelligence Research (2019) Vol. 64, pp. 645-703
Open Access | Times Cited: 227
Automated Search for new Quantum Experiments
Mario Krenn, Mehul Malik, Robert Fickler, et al.
Physical Review Letters (2016) Vol. 116, Iss. 9
Open Access | Times Cited: 226
Reinforcement learning in sustainable energy and electric systems: a survey
Ting Yang, Liyuan Zhao, Wei Li, et al.
Annual Reviews in Control (2020) Vol. 49, pp. 145-163
Closed Access | Times Cited: 194
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning
Mei Wang, Weihong Deng
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 9319-9328
Closed Access | Times Cited: 189
Attention-Aware Deep Reinforcement Learning for Video Face Recognition
Yongming Rao, Jiwen Lu, Jie Zhou
(2017), pp. 3951-3960
Closed Access | Times Cited: 187
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Joel Z. Leibo, Vinícius Zambaldi, Marc Lanctot, et al.
arXiv (Cornell University) (2017), pp. 464-473
Closed Access | Times Cited: 181
Reinforcement learning in robotic applications: a comprehensive survey
Bharat Singh, Rajesh Kumar, V. P. Singh
Artificial Intelligence Review (2021) Vol. 55, Iss. 2, pp. 945-990
Closed Access | Times Cited: 170
Deep Reinforcement Learning
Chong Li
Chapman and Hall/CRC eBooks (2019), pp. 125-154
Open Access | Times Cited: 165
Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models
Zhenzhen Hu, Wenyin Gong, Shuijia Li
Energy Reports (2021) Vol. 7, pp. 916-928
Open Access | Times Cited: 106
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management
Vijendra Kumar, Hazi Mohammad Azamathulla, Kul Vaibhav Sharma, et al.
Sustainability (2023) Vol. 15, Iss. 13, pp. 10543-10543
Open Access | Times Cited: 105
Toward Human-in-the-Loop AI: Enhancing Deep Reinforcement Learning via Real-Time Human Guidance for Autonomous Driving
Jingda Wu, Zhiyu Huang, Zhongxu Hu, et al.
Engineering (2022) Vol. 21, pp. 75-91
Open Access | Times Cited: 97
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