  In this paper, we introduce a new reinforcement learning (RL) based neural
architecture search (NAS) methodology for effective and efficient generative
adversarial network (GAN) architecture search. The key idea is to formulate the
GAN architecture search problem as a Markov decision process (MDP) for smoother
architecture sampling, which enables a more effective RL-based search algorithm
by targeting the potential global optimal architecture. To improve efficiency,
we exploit an off-policy GAN architecture search algorithm that makes efficient
use of the samples generated by previous policies. Evaluation on two standard
benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed
method is able to discover highly competitive architectures for generally
better image generation results with a considerably reduced computational
burden: 7 GPU hours. Our code is available at
https://github.com/Yuantian013/E2GAN.
