In "normal" machine learning this is basically hyperparmater optimization for a given dataset (eg, the depth of a random forest, XGB parameters, the best random seed/jk )
In this case is tests different combinations of operators on a known dataset to see what performs the best. So it is optimizing the prediction network
(Also this isn't DeepRL, it's a deep neural network. I think that was a typo)
[1] https://research.googleblog.com/2017/05/using-machine-learni...
Also it seems different from more traditional hyperparameter optimization because it makes novel cells. So the structure of the network isn't limited to our existing library of layers/cells.
https://arxiv.org/abs/1611.01578 https://youtu.be/HcStlHGpjN8?t=2073
It's entirely true that these are combinations that humans haven't (and probably wouldn't) come up with.
I don't want to underplay this. "It's similar to hyperparameter search" makes it sound like it isn't interesting or novel, which is untrue. I completely believe it is a revolutionary way to build software (so much so that I quit my job, raised funding and are working on a similar space of problems).
But it isn't doing something like inventing a new math operations similar to the other operators which humans put together to form cells/layers. It is rearranging and choosing those operators in new ways.
Why doesn't this just move the optimization problem? Aren't you now just optimizing your DeepRL network rather than the network you're trying to optimize?