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Then, I will discuss two formulations of meta learning.
I will present several meta learning algorithms that can quickly solve simulated robotics tasks, and show how a simple meta learning approach can address the sim2real problem in robotics.
In this talk, I will present meta learning for control: policy learning algorithms that can themselves generate algorithms that are highly customized towards a certain domain of tasks.
Finally, I will conclude with several promising future directions of meta learning for control.
Dissertation talk on meta learning for control: policy learning algorithms that can themselves generate algorithms that are highly customized towards a certain domain of tasks.
In the first part, I will talk about meta learning, which is the problem of training a system that quickly learns to solve a wide variety of tasks.
Similar(54)
Many ensembling methods are meta-learning techniques, thus they can be used to design ensembles of various member classifiers.
Future work will also investigate the ability to extend the learning process inside the SOHN as a form of meta- learning.
The researchers found that by using meta-learning, their sumo-bots could learn effective strategies more quickly.
The application of meta-learning led to an increase in the values of evaluating parameters.
The experiments also show how easy it is to use PLCG as a meta-learning strategy to explore different parts of the space of rule models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com