A comprehensive survey on safe reinforcement learning

655 indexed citations
published 2015

Countries where authors are citing A comprehensive survey on safe reinforcement learning

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Citations

This map shows the geographic impact of A comprehensive survey on safe reinforcement learning. 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 A comprehensive survey on safe reinforcement learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A comprehensive survey on safe reinforcement learning more than expected).

Fields of papers citing A comprehensive survey on safe reinforcement learning

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of A comprehensive survey on safe reinforcement learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A comprehensive survey on safe reinforcement learning.

About A comprehensive survey on safe reinforcement learning

This paper, published in 2015, received 655 indexed citations . Written by Javier García and Fernando Fernández covering the research area of Software, Control and Systems Engineering and Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (393 citations), Control and Systems Engineering (291 citations) and Automotive Engineering (122 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/w5168308.

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