2021
DOI: 10.2514/1.i010856
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Fast Path Planning for Unmanned Aerial Vehicles by Self-Correction Based on Q-Learning

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Cited by 4 publications
(1 citation statement)
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“…For both objective measures – that is the measure of the level of expertise before starting to learn and the writing performance during learning – the students' writing was assessed in terms of the percentage correctness of each Japanese letter in each of the three sections (3 × 3 letters). The percentage correctness was calculated as the total correctness of the shape on the X‐ and Y‐coordinates of the tablet and the configuration and order of single strokes, required to write a letter (see also Wang et al, 2014). To assist in this calculation, an algorithm based on an $N‐protractor was used (Anthony & Wobbrock, 2012).…”
Section: Methodsmentioning
confidence: 99%
“…For both objective measures – that is the measure of the level of expertise before starting to learn and the writing performance during learning – the students' writing was assessed in terms of the percentage correctness of each Japanese letter in each of the three sections (3 × 3 letters). The percentage correctness was calculated as the total correctness of the shape on the X‐ and Y‐coordinates of the tablet and the configuration and order of single strokes, required to write a letter (see also Wang et al, 2014). To assist in this calculation, an algorithm based on an $N‐protractor was used (Anthony & Wobbrock, 2012).…”
Section: Methodsmentioning
confidence: 99%