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A population-based approach for multi-agent interpretable reinforcement learning

Overview of attention for article published in Applied Soft Computing, November 2023
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
7 X users

Citations

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4 Dimensions

Readers on

mendeley
8 Mendeley
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Title
A population-based approach for multi-agent interpretable reinforcement learning
Published in
Applied Soft Computing, November 2023
DOI 10.1016/j.asoc.2023.110758
Authors

Marco Crespi, Andrea Ferigo, Leonardo Lucio Custode, Giovanni Iacca

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 13%
Lecturer 1 13%
Unknown 6 75%
Readers by discipline Count As %
Computer Science 1 13%
Unknown 7 88%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 30 August 2023.
All research outputs
#6,361,238
of 25,394,764 outputs
Outputs from Applied Soft Computing
#107
of 1,068 outputs
Outputs of similar age
#89,151
of 357,185 outputs
Outputs of similar age from Applied Soft Computing
#3
of 24 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,068 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done well, scoring higher than 89% 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 357,185 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 74% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.