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Mobile robots exploration through cnn-based reinforcement learning

Overview of attention for article published in Robotics and Biomimetics, December 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

twitter
2 X users
patent
1 patent

Citations

dimensions_citation
85 Dimensions

Readers on

mendeley
83 Mendeley
Title
Mobile robots exploration through cnn-based reinforcement learning
Published in
Robotics and Biomimetics, December 2016
DOI 10.1186/s40638-016-0055-x
Pubmed ID
Authors

Lei Tai, Ming Liu

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 20%
Student > Bachelor 13 16%
Student > Master 10 12%
Researcher 5 6%
Professor 4 5%
Other 13 16%
Unknown 21 25%
Readers by discipline Count As %
Computer Science 27 33%
Engineering 27 33%
Unspecified 2 2%
Business, Management and Accounting 2 2%
Social Sciences 2 2%
Other 2 2%
Unknown 21 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 10 June 2021.
All research outputs
#7,126,547
of 25,608,265 outputs
Outputs from Robotics and Biomimetics
#4
of 40 outputs
Outputs of similar age
#120,052
of 424,016 outputs
Outputs of similar age from Robotics and Biomimetics
#2
of 2 outputs
Altmetric has tracked 25,608,265 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 40 research outputs from this source. They receive a mean Attention Score of 1.9. This one scored the same or higher as 36 of them.
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 424,016 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.