Continuous control with deep reinforcement learning
- Journal
- arXiv (Cornell University)
In The Last Decade
doi.org/w3779662 →Countries where authors are citing Continuous control with deep reinforcement learning
This map shows the geographic impact of Continuous control with deep 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 Continuous control with deep 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 Continuous control with deep reinforcement learning more than expected).
Fields of papers citing Continuous control with deep reinforcement learning
This network shows the impact of Continuous control with deep 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 Continuous control with deep reinforcement learning.
About Continuous control with deep reinforcement learning
This paper, published in 2016, received 4.9k indexed citations . Written by Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver and Daan Wierstra covering the research area of Statistical and Nonlinear Physics and Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (2.1k citations), Electrical and Electronic Engineering (1.4k citations) and Control and Systems Engineering (1.4k citations). Published in arXiv (Cornell University).
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/w3779662.