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.
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...