Model-based Reinforcement Learning: A Survey
- Journal
- Research Repository (Delft University of Technology)
In The Last Decade
doi.org/10.1561/2200000086 →Countries where authors are citing Model-based Reinforcement Learning: A Survey
This map shows the geographic impact of Model-based Reinforcement Learning: A Survey. 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 Model-based Reinforcement Learning: A Survey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Model-based Reinforcement Learning: A Survey more than expected).
Fields of papers citing Model-based Reinforcement Learning: A Survey
This network shows the impact of Model-based Reinforcement Learning: A Survey. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Model-based Reinforcement Learning: A Survey.
About Model-based Reinforcement Learning: A Survey
This paper, published in 2023, received 220 indexed citations . Written by Thomas M. Moerland, Joost Broekens, Aske Plaat and Catholijn M. Jonker covering the research area of Artificial Intelligence and Management Science and Operations Research. It is primarily cited by scholars working on Artificial Intelligence (92 citations), Control and Systems Engineering (54 citations) and Electrical and Electronic Engineering (47 citations). Published in Research Repository (Delft University of Technology).
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/10.1561/2200000086.