| Title |
The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces
|
|---|---|
| Published in |
Machine Learning, December 1995
|
| DOI | 10.1007/bf00993591 |
| Authors |
Andrew W. Moore, Christopher G. Atkeson |
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 States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Italy | 2 | 3% |
| Germany | 2 | 3% |
| Netherlands | 1 | 1% |
| India | 1 | 1% |
| Switzerland | 1 | 1% |
| United Kingdom | 1 | 1% |
| Slovakia | 1 | 1% |
| Canada | 1 | 1% |
| Other | 5 | 6% |
| Unknown | 62 | 78% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 38% |
| Researcher | 14 | 18% |
| Student > Master | 9 | 11% |
| Student > Doctoral Student | 7 | 9% |
| Other | 5 | 6% |
| Other | 13 | 16% |
| Unknown | 2 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 44 | 55% |
| Engineering | 16 | 20% |
| Mathematics | 2 | 3% |
| Agricultural and Biological Sciences | 2 | 3% |
| Business, Management and Accounting | 2 | 3% |
| Other | 5 | 6% |
| Unknown | 9 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 17 October 2017.
All research outputs
#18,733,166
of 23,885,338 outputs
Outputs from Machine Learning
#790
of 1,029 outputs
Outputs of similar age
#78,450
of 80,932 outputs
Outputs of similar age from Machine Learning
#7
of 8 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,029 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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 80,932 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.