Unifying count-based exploration and intrinsic motivation
- Authors
- Marc G. BellemareSriram SrinivasanGeorg OstrovskiTom SchaulDavid Saxton
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w8740301 →Countries where authors are citing Unifying count-based exploration and intrinsic motivation
This map shows the geographic impact of Unifying count-based exploration and intrinsic motivation. 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 Unifying count-based exploration and intrinsic motivation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Unifying count-based exploration and intrinsic motivation more than expected).
Fields of papers citing Unifying count-based exploration and intrinsic motivation
This network shows the impact of Unifying count-based exploration and intrinsic motivation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Unifying count-based exploration and intrinsic motivation.
About Unifying count-based exploration and intrinsic motivation
This paper, published in 2016, received 280 indexed citations . Written by Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton and Rémi Munos covering the research area of Artificial Intelligence and Economics and Econometrics. It is primarily cited by scholars working on Artificial Intelligence (243 citations), Computer Vision and Pattern Recognition (57 citations) and Management Science and Operations Research (42 citations). Published in Neural Information Processing Systems.
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/w8740301.