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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:

Explainable Reinforcement Learning: A Survey and Comparative Review
Stephanie Milani, Nicholay Topin, Manuela Veloso, et al.
ACM Computing Surveys (2023) Vol. 56, Iss. 7, pp. 1-36
Open Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, et al.
IEEE Access (2024) Vol. 12, pp. 101603-101625
Open Access | Times Cited: 48
Machine learning applications in nanomaterials: Recent advances and future perspectives
Liang Yang, Hong Wang, Deying Leng, et al.
Chemical Engineering Journal (2024), pp. 156687-156687
Closed Access | Times Cited: 22
Integrating machine learning and biosensors in microfluidic devices: A review
Gianni Antonelli, Joanna Filippi, Michele D’Orazio, et al.
Biosensors and Bioelectronics (2024) Vol. 263, pp. 116632-116632
Open Access | Times Cited: 10
XLight: An interpretable multi-agent reinforcement learning approach for traffic signal control
Sibin Cai, Jie Fang, Mengyun Xu
Expert Systems with Applications (2025), pp. 126938-126938
Closed Access | Times Cited: 1
Neurosymbolic Reinforcement Learning and Planning: A Survey
K. Acharya, Waleed Raza, Carlos Mauricio Jaborandy de Mattos Dourado, et al.
IEEE Transactions on Artificial Intelligence (2023) Vol. 5, Iss. 5, pp. 1939-1953
Open Access | Times Cited: 12
On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data
Tomasz Hachaj, Marcin Piekarczyk
Applied Sciences (2025) Vol. 15, Iss. 2, pp. 538-538
Open Access
Segmentation of Human Motion Segments Based on Spatio-Temporal Feature Extraction of Motion Data
子昊 宋
Artificial Intelligence and Robotics Research (2025) Vol. 14, Iss. 01, pp. 138-153
Closed Access
Evolving adaptive and interpretable decision trees for cooperative submarine search
Yang Gao, Yue Wang, Lingyun Tian, et al.
Defence Technology (2025)
Open Access
Multi-robot hierarchical safe reinforcement learning autonomous decision-making strategy based on uniformly ultimate boundedness constraints
Huihui Sun, Hui Jiang, Long Zhang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Explainable reinforcement learning for powertrain control engineering
Claude Laflamme, J. Doppler, B. Palvolgyi, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 146, pp. 110135-110135
Open Access
Cross-modal feature symbiosis for personalized meta-path generation in heterogeneous networks
Xiaotong Wu, Liqing Qiu, Weidong Zhao
Neurocomputing (2025), pp. 129780-129780
Closed Access
Deep Reinforcement Learning for Facilitating Human-Robot Interaction in Manufacturing
Nathan Eskue, Márcia Baptista
Springer series in advanced manufacturing (2025), pp. 69-95
Closed Access
The Value of Real-time Automated Explanations in Stochastic Planning
Claudia V. Goldman, Ronit Bustin, Wenyuan Qi, et al.
Artificial Intelligence (2025), pp. 104323-104323
Closed Access
Research Progress and Prospect of High-Speed Vehicle Control Technology Based on Reinforcement Learning
Kunfeng Lu, Weili Wang, Xiaodong Liu, et al.
(2025)
Closed Access
Interpretable multi-agent reinforcement learning via multi-head variational autoencoders
Peizhang Li, Qing Fei, Zhen Chen
Applied Intelligence (2025) Vol. 55, Iss. 7
Closed Access
Scheduling Reentrant FlowShops: Reinforcement Learning‐guided Meta‐Heuristics
J.Q. Yuan, Kaizhou Gao, Adam Słowik, et al.
IET Collaborative Intelligent Manufacturing (2025) Vol. 7, Iss. 1
Open Access
A deep reinforcement active learning method for multi-label image classification
Qing Cai, Ran Tao, Xiufen Fang, et al.
Computer Vision and Image Understanding (2025), pp. 104351-104351
Closed Access
Experiential Explanations for Reinforcement Learning
Amal Alabdulkarim, Madhuri Singh, Gennie Mansi, et al.
Neural Computing and Applications (2025)
Open Access
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations
Indu Panigrahi, Sunnie S. Y. Kim, Amna Liaqat, et al.
(2025), pp. 1-9
Closed Access
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI
Bálint Gyevnár, Stephanie Droop, Tadeg Quillien, et al.
(2025), pp. 1-18
Closed Access
Why Reinforcement Learning?
Mehmet Emin Aydın, Rafet Durgut, Abdur Rakib
Algorithms (2024) Vol. 17, Iss. 6, pp. 269-269
Open Access | Times Cited: 2
Explaining the Space of SSP Policies via Policy-Property Dependencies: Complexity, Algorithms, and Relation to Multi-Objective Planning
Marcel Steinmetz, Sylvie Thiébaux, Daniel Höller, et al.
Proceedings of the International Conference on Automated Planning and Scheduling (2024) Vol. 34, pp. 555-564
Open Access | Times Cited: 1
Combining AI control systems and human decision support via robustness and criticality
Walt Woods, Alexander Grushin, Simon Khan, et al.
(2024)
Open Access | Times Cited: 1
A Review on the Form and Complexity of Human–Robot Interaction in the Evolution of Autonomous Surgery
Tangyou Liu, Jiaole Wang, Shing Wai Wong, et al.
Advanced Intelligent Systems (2024) Vol. 6, Iss. 11
Open Access | Times Cited: 1
LearningTuple: A packet classification scheme with high classification and high update
Zhuo Li, Nan Zhang, Hao Xun, et al.
Computer Networks (2024) Vol. 254, pp. 110745-110745
Closed Access | Times Cited: 1
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