Open Access Helper

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:

Phasic Policy Gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 49

Showing 26-50 of 49 citing articles:

Learning from Experience
Christopher Mutschler, Γεώργιος Κόντες, Sebastian Rietsch
(2024), pp. 49-76
Closed Access
Multi-Task Decision-Making for Multi-User $360^{\circ}$ Video Processing over Wireless Networks
Babak Badnava, Jacob Chakareski, Morteza Hashemi
(2024), pp. 294-300
Closed Access
Policy Optimization with Augmented Value Targets for Generalization in Reinforcement Learning
Nasik Muhammad Nafi, Giovanni Poggi-Corradini, William Hsu
2022 International Joint Conference on Neural Networks (IJCNN) (2023), pp. 1-8
Closed Access | Times Cited: 1
Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training
Gang Chen, Victoria Huang
(2023), pp. 3514-3522
Open Access | Times Cited: 1
Deep Reinforcement Learning Based Collision Avoidance System for Autonomous Ships
Yong Wang, Haixiang Xu, Hui Feng, et al.
(2023)
Closed Access | Times Cited: 1
Machine Learning Meets Advanced Robotic Manipulation
Saeid Nahavandi, Roohallah Alizadehsani, Darius Nahavandi, et al.
(2023)
Open Access | Times Cited: 1
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation
Emilio Parisotto, Russ R. Salakhutdinov
International Conference on Learning Representations (2021)
Closed Access | Times Cited: 2
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration
John Mern, Kyle Hatch, Ryan Silva, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 2
Phasic Policy Gradient Based Resource Allocation for Industrial Internet of Things
Lokesh Bommisetty, Venkatesh Tiruchirai Gopalakrishnan
(2022), pp. 501-502
Open Access | Times Cited: 1
Continuous self‐adaptation of control policies in automatic cloud management
Włodzimierz Funika, Paweł Koperek, Jacek Kitowski
Concurrency and Computation Practice and Experience (2022) Vol. 35, Iss. 20
Closed Access | Times Cited: 1
Meta Proximal Policy Optimization for Cooperative Multi-Agent Continuous Control
Boli Fang, Zhenghao Peng, Hao Sun, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2022), pp. 1-8
Closed Access | Times Cited: 1
Decoupling Value and Policy for Generalization in Reinforcement Learning
Roberta Raileanu, Rob Fergus
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 1
Dynamic deployment of multi‐UAV base stations with deep reinforcement learning
Guanhan Wu, Weimin Jia, Jianwei Zhao
Electronics Letters (2021) Vol. 57, Iss. 15, pp. 600-602
Open Access | Times Cited: 1
Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation
Christopher R. Dance, Julien Perez, Théo Cachet
International Conference on Machine Learning (2021), pp. 2376-2387
Closed Access | Times Cited: 1
RL-DARTS: Differentiable Architecture Search for Reinforcement Learning.
Yingjie Miao, Xingyou Song, Daiyi Peng, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 1
Multi-Agent Deep Reinforcement Learning for Walkers
Inhee Park
(2020)
Open Access
Optimizations over neural MCTS for solving combinatorial problems
Prashank Kadam
(2020)
Open Access
Learning to drive from a world on rails
Dian Chen, Vladlen Koltun, Philipp Krähenbühl
arXiv (Cornell University) (2021)
Closed Access
Mix and Mask Actor-Critic Methods
Dom Huh
arXiv (Cornell University) (2021)
Open Access
Learning to Design and Construct Bridge without Blueprint
Yunfei Li, Tao Kong, Lei Li, et al.
arXiv (Cornell University) (2021)
Open Access
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, et al.
arXiv (Cornell University) (2021)
Closed Access
Batch size-invariance for policy optimization.
Jacob Hilton, Karl Cobbe, John Schulman
arXiv (Cornell University) (2021)
Closed Access
Multi-Agent Deep Reinforcement Learning for Walker Systems
Inhee Park, Teng-Sheng Moh
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2021), pp. 490-495
Closed Access
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