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In this article we propose its application to the task of exploring unknown environments.
It is promising to see that all the runs based on RDE achieved better F1 than SVM (11) and logistic regression (12), and the incorporation of RDE produced significant improvement on F1comparing the performance of Logistic regression (F1 17.4%andd 28.6%) with the best run with RDE (F1 22.1% and 35.7%), which justified the success of the application of RDE to this task.
We identified several features, however, that could be added to such an application in order to tailor it to this task.
It thus enables the application engineer to automate this task which is still carried out mostly manually in practice.
This might help to organize new facts in a more efficient way leading to a better application to procedural knowledge tasks.
There is a number of applications for this task.
In the '70s, J.M. Hurst wrote a book about the application of cycles to assist in this task, which led me to an interest in cycles.
To accomplish this task, an application referring to a segment of a typical Italian highway is proposed.
The values obtained are comparable with other applications of Mallet to this same task, for example, [10].
Furthermore, our framework provides straightforward applications to tasks that do not invoke explicit rewards or support value learning, such as the urn task (FitzGerald et al. in review).
This design allows the embedded application to be scheduled for various tasks with less memory footprint.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com