Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 154

Showing 1-25 of 154 citing articles:

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine, Aviral Kumar, George Tucker, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 733
Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar, Aurick Zhou, George Tucker, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 457
How to train your robot with deep reinforcement learning: lessons we have learned
Julian Ibarz, Jie Tan, Chelsea Finn, et al.
The International Journal of Robotics Research (2021) Vol. 40, Iss. 4-5, pp. 698-721
Open Access | Times Cited: 382
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lü, Aravind Rajeswaran, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 327
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, et al.
Machine Learning (2021) Vol. 110, Iss. 9, pp. 2419-2468
Open Access | Times Cited: 320
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
Justin Fu, Aviral Kumar, Ofir Nachum, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 319
Learning Agile Robotic Locomotion Skills by Imitating Animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang, et al.
(2020)
Open Access | Times Cited: 277
MOPO: Model-based Offline Policy Optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 210
A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto, Shixiang Gu
arXiv (Cornell University) (2021)
Open Access | Times Cited: 133
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
Rafael Figueiredo Prudencio, Marcos R. O. A. Máximo, Esther Luna Colombini
IEEE Transactions on Neural Networks and Learning Systems (2023) Vol. 35, Iss. 8, pp. 10237-10257
Open Access | Times Cited: 133
Offline Reinforcement Learning with Implicit Q-Learning
Ilya Kostrikov, Ashvin Nair, Sergey Levine
arXiv (Cornell University) (2021)
Open Access | Times Cited: 107
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 98
Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Żołna, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 88
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 62
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World
Laura Smith, J. Chase Kew, Xue Bin Peng, et al.
2022 International Conference on Robotics and Automation (ICRA) (2022), pp. 1593-1599
Open Access | Times Cited: 49
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Paria Rashidinejad, Banghua Zhu, Cong Ma, et al.
IEEE Transactions on Information Theory (2022) Vol. 68, Iss. 12, pp. 8156-8196
Open Access | Times Cited: 40
Evolutionary Learning of Interpretable Decision Trees
Leonardo Lucio Custode, Giovanni Iacca
IEEE Access (2023) Vol. 11, pp. 6169-6184
Open Access | Times Cited: 35
HIVE: Harnessing Human Feedback for Instructional Visual Editing
Shu Zhang, Xinyi Yang, Yihao Feng, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) Vol. 34, pp. 9026-9036
Closed Access | Times Cited: 10
Dynamic optimizers for complex industrial systems via direct data-driven synthesis
Khalid Alhazmi, S. Mani Sarathy
Communications Engineering (2025) Vol. 4, Iss. 1
Open Access | Times Cited: 1
PIRLNav: Pretraining with Imitation and RL Finetuning for OBJECTNAV
Ram Ramrakhya, Dhruv Batra, Erik Wijmans, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Open Access | Times Cited: 19
Phasic Policy Gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 49
An empirical investigation of the challenges of real-world reinforcement learning.
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 48
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, et al.
International Conference on Learning Representations (2021)
Closed Access | Times Cited: 37
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Çağlar Gülçehre, Ziyu Wang, Alexander Novikov, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 38
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Avi Singh, Albert S. Yu, T. Jonathan Yang, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 33
Page 1 - Next Page

Scroll to top