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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:

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

Showing 26-50 of 98 citing articles:

Smart Management of Electric Vehicle Chargers Through Reinforcement Learning
Heba M. Abdullah, Adel Gastli, Lazhar Ben‐Brahim
(2024), pp. 1-8
Closed Access | Times Cited: 1
Parallel Cross Entropy Policy Gradient Adaptive Dynamic Programming for Optimal Tracking Control of Discrete-Time Nonlinear Systems
Jiahui Xu, Jingcheng Wang, Jun Rao, et al.
IEEE Transactions on Systems Man and Cybernetics Systems (2024) Vol. 54, Iss. 6, pp. 3809-3821
Closed Access | Times Cited: 1
RIIT: Rethinking the Importance of Implementation Tricks in Multi-Agent Reinforcement Learning.
Jian Hu, Haibin Wu, Seth Austin Harding, et al.
(2021)
Closed Access | Times Cited: 10
Learning High Speed Precision Table Tennis on a Physical Robot
Tianli Ding, Laura Graesser, Saminda Abeyruwan, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022), pp. 10780-10787
Closed Access | Times Cited: 7
Zero-Shot Terrain Generalization for Visual Locomotion Policies
Alejandro Escontrela, George Yu, Peng Xu, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 9
Understanding reinforcement learned crowds
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré, et al.
Computers & Graphics (2022) Vol. 110, pp. 28-37
Open Access | Times Cited: 6
Reinforcement learning policy recommendation for interbank network stability
Alessio Brini, Gabriele Tedeschi, Daniele Tantari
Journal of Financial Stability (2023) Vol. 67, pp. 101139-101139
Open Access | Times Cited: 3
An off-policy multi-agent stochastic policy gradient algorithm for cooperative continuous control
Delin Guo, Lan Tang, Xinggan Zhang, et al.
Neural Networks (2023) Vol. 170, pp. 610-621
Closed Access | Times Cited: 3
What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 8, pp. 8441-8449
Open Access | Times Cited: 5
Average-Reward Reinforcement Learning with Trust Region Methods
Xiaoteng Ma, Xiaohang Tang, Li Xia, et al.
(2021), pp. 2797-2803
Open Access | Times Cited: 7
Differentiable Trust Region Layers for Deep Reinforcement Learning
Fabian Otto, Philipp Becker, Vien Anh Ngo, et al.
International Conference on Learning Representations (2021)
Closed Access | Times Cited: 6
PPOAccel: A High-Throughput Acceleration Framework for Proximal Policy Optimization
Yuan Meng, Sanmukh R. Kuppannagari, Rajgopal Kannan, et al.
IEEE Transactions on Parallel and Distributed Systems (2021) Vol. 33, Iss. 9, pp. 2066-2078
Open Access | Times Cited: 6
Offline Quantum Reinforcement Learning in a Conservative Manner
Zhihao Cheng, Kaining Zhang, Li Shen, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2023) Vol. 37, Iss. 6, pp. 7148-7156
Open Access | Times Cited: 2
Planning Multiple Epidemic Interventions with Reinforcement Learning
Anh Mai, Nikunj Gupta, Azza Abouzied, et al.
(2023), pp. 6147-6155
Open Access | Times Cited: 2
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms
Shangtong Zhang, Romain Laroche, Harm van Seijen, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 5
How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget.
Erik Wijmans, Irfan Essa, Dhruv Batra
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 5
First-Order Problem Solving through Neural MCTS based Reinforcement Learning
Ruiyang Xu, Prashank Kadam, Karl Lieberherr
arXiv (Cornell University) (2021)
Open Access | Times Cited: 5
Revisiting Design Choices in Proximal Policy Optimization.
Chloe Hsu, Celestine Mendler-Dünner, Moritz Hardt
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 4
A Pragmatic Look at Deep Imitation Learning
Kai Arulkumaran, Dan Ogawa Lillrank
arXiv (Cornell University) (2021)
Open Access | Times Cited: 4
Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent
Adrien Bolland, Ioannis Boukas, Mathias Berger, et al.
Journal of Artificial Intelligence Research (2022) Vol. 73, pp. 117-171
Open Access | Times Cited: 3
Crystallization Process Design by Model-Free Deep Reinforcement Learning
Georgi Tancev
(2024)
Open Access
Crystallization Process Design by Model-Free Deep Reinforcement Learning
Georgi Tancev
(2024)
Open Access
What Do European Futures Look Like According to Fine-Tuned Gpt-3, Prompt-Tuned Gpt-3.5, and Few-Shot Prompted Gpt-4?
Diana Kozachek
(2024)
Closed Access
Comparing Reinforcement Learning and Human Learning With the Game of Hidden Rules
Eric Pulick, Vladimir Meñkov, Yonatan Mintz, et al.
IEEE Access (2024) Vol. 12, pp. 65362-65372
Open Access
Crystallization Process Design by Proximal Policy Optimization
Georgi Tancev
(2024)
Open Access

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