  The neural architecture search (NAS) algorithm with reinforcement learning
can be a powerful and novel framework for the automatic discovering process of
neural architectures. However, its application is restricted by noncontinuous
and high-dimensional search spaces, which result in difficulty in optimization.
To resolve these problems, we proposed NAS in embedding space (NASES), which is
a novel framework. Unlike other NAS with reinforcement learning approaches that
search over a discrete and high-dimensional architecture space, this approach
enables reinforcement learning to search in an embedding space by using
architecture encoders and decoders. The current experiment demonstrated that
the performance of the final architecture network using the NASES procedure is
comparable with that of other popular NAS approaches for the image
classification task on CIFAR-10. The results of the experiment were efficient
and indicated that NASES was highly efficient to discover final architecture
only in $<$3.5 GPU hours. The beneficial-performance and effectiveness of NASES
was impressive when the architecture-embedding searching and weight
initialization were applied.
