Neural architecture search (NAS) is a method of automating the design (that is, the choice of the values of the hyper-parameters) of artificial neural networks. There are different approaches to search the space of neural network architectures. For example, you can use reinforcement learning or evolutionary (or genetic) algorithms.
Check out the paper Neural Architecture Search with Reinforcement Learning (2017), by Barret Zoph and Quoc V. Le, where the authors train, using reinforcement learning (specifically, REINFORCE), a recurrent neural network (the "controller") to generate (convolutional and recurrent) neural network architectures, so that to maximise the expected accuracy of the generated architectures on a validation dataset. They achieve some good results using this approach.
See also Efficient Neural Architecture Search via Parameter Sharing (2018), by Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le and Jeff Dean (which thus includes some of the authors of NAS), which is similar to NAS, but more efficient, hence the acronym ENAS (efficient NAS).