Stable-Baselines3: Reliable Reinforcement Learning Implementations

769 indexed citations
published 2021

Countries where authors are citing Stable-Baselines3: Reliable Reinforcement Learning Implementations

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This map shows the geographic impact of Stable-Baselines3: Reliable Reinforcement Learning Implementations. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stable-Baselines3: Reliable Reinforcement Learning Implementations with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stable-Baselines3: Reliable Reinforcement Learning Implementations more than expected).

Fields of papers citing Stable-Baselines3: Reliable Reinforcement Learning Implementations

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Stable-Baselines3: Reliable Reinforcement Learning Implementations. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Stable-Baselines3: Reliable Reinforcement Learning Implementations.

About Stable-Baselines3: Reliable Reinforcement Learning Implementations

This paper, published in 2021, received 769 indexed citations . Written by Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto and Maximilian Ernestus. It is primarily cited by scholars working on Artificial Intelligence (288 citations), Control and Systems Engineering (220 citations), Electrical and Electronic Engineering (167 citations), Computer Vision and Pattern Recognition (120 citations) and Computer Networks and Communications (101 citations). Published in Journal of Machine Learning Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w3035326.

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