| Title |
Self-improving reactive agents based on reinforcement learning, planning and teaching
|
|---|---|
| Published in |
Machine Learning, May 1992
|
| DOI | 10.1007/bf00992699 |
| Authors |
Long-Ji Lin |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 246 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 3% |
| Portugal | 3 | 1% |
| Italy | 2 | <1% |
| India | 2 | <1% |
| China | 2 | <1% |
| United Kingdom | 2 | <1% |
| Ireland | 1 | <1% |
| Czechia | 1 | <1% |
| Slovakia | 1 | <1% |
| Other | 5 | 2% |
| Unknown | 220 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 62 | 25% |
| Student > Master | 52 | 21% |
| Researcher | 25 | 10% |
| Student > Bachelor | 18 | 7% |
| Student > Doctoral Student | 13 | 5% |
| Other | 41 | 17% |
| Unknown | 35 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 124 | 50% |
| Engineering | 38 | 15% |
| Neuroscience | 8 | 3% |
| Agricultural and Biological Sciences | 7 | 3% |
| Unspecified | 5 | 2% |
| Other | 23 | 9% |
| Unknown | 41 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 03 June 2023.
All research outputs
#3,845,801
of 23,098,660 outputs
Outputs from Machine Learning
#101
of 978 outputs
Outputs of similar age
#1,971
of 19,339 outputs
Outputs of similar age from Machine Learning
#3
of 6 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 978 research outputs from this source. They receive a mean Attention Score of 4.3. 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 19,339 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.