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In this paper, we explore the use of reinforcement learning to "shape" a robot to perform a predefined target behavior.
The assessment provides an estimate of the risk in monetary value and determines whether the estimated risk exceeds a predefined target risk.
Results from several simulations show that the proposed method enables the robot to find an optimal trajectory from any given state towards a predefined target.
Results from several simulated experiments show that the proposed approach is capable of finding an optimal path from any given position/orientation towards a predefined target in an environment with obstacles within the admissible region.
Participants had to recognize as fast as possible (go/no-go paradigm) a predefined target, belonging to one of two superordinate categories.
Scoring function g2(C) The second scoring function, g2(C), evaluates how much closer the relative frequency of clusters of a size in C and a predefined target relative frequency of the size, which is given as a parameter.
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The predefined target was a body mass index (BMI) of 17 kg/m.
This value-pair maps via the predefined target function to an unique value, f (x, y), which can be considered as the solution to the particular problem presented by a prey.
This work presents a new steerable needle to facilitate active steering toward predefined target locations.
From a technical point of view, the PCR and microarray can detect only predefined target species, whereas PCR and sequencing possibly detects all eubacterial species.
The similarity in surface roughness between the predefined target road surface and the laboratory surface samples was evaluated using optical 3D surface roughness measurements and surface roughness power spectrums.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com