| Title |
Human-level control through deep reinforcement learning
|
|---|---|
| Published in |
Nature, February 2015
|
| DOI | 10.1038/nature14236 |
| Pubmed ID | |
| Authors |
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis |
X Demographics
The data shown below were collected from the profiles of 857 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 174 | 20% |
| Japan | 71 | 8% |
| United Kingdom | 68 | 8% |
| Canada | 25 | 3% |
| Spain | 19 | 2% |
| Germany | 18 | 2% |
| France | 16 | 2% |
| Netherlands | 14 | 2% |
| India | 12 | 1% |
| Other | 96 | 11% |
| Unknown | 344 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 617 | 72% |
| Scientists | 210 | 25% |
| Science communicators (journalists, bloggers, editors) | 17 | 2% |
| Practitioners (doctors, other healthcare professionals) | 13 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 14,029 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 94 | <1% |
| United Kingdom | 48 | <1% |
| Germany | 34 | <1% |
| Japan | 24 | <1% |
| China | 22 | <1% |
| Spain | 18 | <1% |
| France | 16 | <1% |
| Canada | 9 | <1% |
| Brazil | 9 | <1% |
| Other | 106 | <1% |
| Unknown | 13649 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3150 | 22% |
| Student > Master | 2538 | 18% |
| Researcher | 1586 | 11% |
| Student > Bachelor | 1306 | 9% |
| Student > Doctoral Student | 502 | 4% |
| Other | 1696 | 12% |
| Unknown | 3251 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4880 | 35% |
| Engineering | 2613 | 19% |
| Agricultural and Biological Sciences | 409 | 3% |
| Physics and Astronomy | 379 | 3% |
| Neuroscience | 374 | 3% |
| Other | 1759 | 13% |
| Unknown | 3615 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1545. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2024.
All research outputs
#7,565
of 25,782,229 outputs
Outputs from Nature
#796
of 98,746 outputs
Outputs of similar age
#52
of 270,925 outputs
Outputs of similar age from Nature
#9
of 987 outputs
Altmetric has tracked 25,782,229 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 98,746 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 102.7. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,925 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 987 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.