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Explainability of deep reinforcement learning algorithms in robotic domains by using Layer-wise Relevance Propagation

Overview of attention for article published in Engineering Applications of Artificial Intelligence, November 2024
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  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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3 X users

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1 Mendeley
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Title
Explainability of deep reinforcement learning algorithms in robotic domains by using Layer-wise Relevance Propagation
Published in
Engineering Applications of Artificial Intelligence, November 2024
DOI 10.1016/j.engappai.2024.109131
Authors

Mehran Taghian, Shotaro Miwa, Yoshihiro Mitsuka, Johannes Günther, Shadan Golestan, Osmar Zaiane

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 100%
Readers by discipline Count As %
Unspecified 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 August 2024.
All research outputs
#15,832,598
of 26,523,931 outputs
Outputs from Engineering Applications of Artificial Intelligence
#408
of 859 outputs
Outputs of similar age
#3,244
of 6,400 outputs
Outputs of similar age from Engineering Applications of Artificial Intelligence
#2
of 7 outputs
Altmetric has tracked 26,523,931 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 859 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 52% 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 6,400 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.