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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:

MOPO: Model-based Offline Policy Optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 210

Showing 26-50 of 210 citing articles:

Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning
Tao Liu, Qi Xu, Wei Shi, et al.
(2025), pp. 2458-2468
Closed Access
Pessimistic policy iteration with bounded uncertainty
Zhiyong Peng, Changlin Han, Yadong Liu, et al.
Expert Systems with Applications (2025), pp. 127651-127651
Closed Access
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
Phillip Swazinna, Steffen Udluft, Daniel Hein, et al.
IFAC-PapersOnLine (2022) Vol. 55, Iss. 15, pp. 19-26
Open Access | Times Cited: 17
Supervised Optimal Chemotherapy Regimen Based on Offline Reinforcement Learning
Chamani Shiranthika, Kuo-Wei Chen, Chung-Yih Wang, et al.
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 9, pp. 4763-4772
Closed Access | Times Cited: 16
Recent advances in path integral control for trajectory optimization: An overview in theoretical and algorithmic perspectives
Muhammad Kazim, JunGee Hong, Min-Gyeom Kim, et al.
Annual Reviews in Control (2024) Vol. 57, pp. 100931-100931
Open Access | Times Cited: 3
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
Yinmin Zhang, Jie Liu, Chuming Li, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 15, pp. 16908-16916
Open Access | Times Cited: 3
COMBO: Conservative Offline Model-Based Policy Optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 21
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
Mila Nambiar, Supriyo Ghosh, Priscilla Ong, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 4673-4684
Open Access | Times Cited: 8
Offline Decentralized Multi-Agent Reinforcement Learning
Jiechuan Jiang, Zongqing Lu
Frontiers in artificial intelligence and applications (2023)
Open Access | Times Cited: 8
Doubly constrained offline reinforcement learning for learning path recommendation
Yun Yue, Huan Dai, Rui An, et al.
Knowledge-Based Systems (2023) Vol. 284, pp. 111242-111242
Closed Access | Times Cited: 8
Is Pessimism Provably Efficient for Offline RL
Ying Jin, Zhuoran Yang, Zhaoran Wang
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 23
Model-Based Offline Planning
Arthur Argenson, Gabriel Dulac-Arnold
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 21
Offline Reinforcement Learning with Reverse Model-based Imagination
Jianhao Wang, Wenzhe Li, Haozhe Jiang, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 18
The Efficacy of Pessimism in Asynchronous Q-Learning
Yuling Yan, Gen Li, Yuxin Chen, et al.
IEEE Transactions on Information Theory (2023) Vol. 69, Iss. 11, pp. 7185-7219
Open Access | Times Cited: 7
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
Jianzhun Du, Joseph Futoma, Finale Doshi‐Velez
arXiv (Cornell University) (2020)
Open Access | Times Cited: 19
The Importance of Pessimism in Fixed-Dataset Policy Optimization
Jacob Buckman, Carles Gelada, Marc G. Bellemare
(2020)
Closed Access | Times Cited: 19
Risk-Averse Offline Reinforcement Learning
Núria Armengol Urpí, Sebastian Curi, Andreas Krause
arXiv (Cornell University) (2021)
Open Access | Times Cited: 17
Offline Reinforcement Learning as Anti-exploration
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 7, pp. 8106-8114
Open Access | Times Cited: 11
Probabilistic design of optimal sequential decision-making algorithms in learning and control
Émiland Garrabé, Giovanni Russo
Annual Reviews in Control (2022) Vol. 54, pp. 81-102
Open Access | Times Cited: 11
A survey of demonstration learning
André Correia, Luı́s A. Alexandre
Robotics and Autonomous Systems (2024), pp. 104812-104812
Open Access | Times Cited: 2
Imitation Learning as State Matching via Differentiable Physics
Si-Wei Chen, Xiao Ma, Zhongwen Xu
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Closed Access | Times Cited: 6
Model-Based Offline Planning
Arthur Argenson, Gabriel Dulac-Arnold
International Conference on Learning Representations (2021)
Closed Access | Times Cited: 14
Text-Based Interactive Recommendation via Offline Reinforcement Learning
Ruiyi Zhang, Tong Yu, Yilin Shen, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 10, pp. 11694-11702
Open Access | Times Cited: 9
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction
Masashi Okada, Tadahiro Taniguchi
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022), pp. 985-991
Open Access | Times Cited: 9
QPLEX: Duplex Dueling Multi-Agent Q-Learning
Jianhao Wang, Zhizhou Ren, Terry Z. Liu, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 14

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