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Distributed GAN: Toward a Faster Reinforcement-Learning-Based Architec by Jiachen Shi, Yi Fan et al

In the existing reinforcement learning (RL)-based neural architecture search (NAS) methods for a generative adversarial network (GAN), both the generator and the discriminator architecture are usually treated as the search objects. In this article, we take a different perspective to propose an approach by treating the generator as the search objective and the discriminator as the judge to evaluate the performance of the generator architecture. Consequently, we can convert this NAS problem to a GAN-style problem, similar to using a controller to generate sequential data via reinforcement learning in a sequence GAN, except that the controller in our methods generates serialized data information of architecture. Furthermore, we adopt an RL-based distributed search method to update the controller parameters θ. Generally, the reward value is calculated after the whole architecture searched, but as another novelty in this article, we employ the reward shaping method to judge the intermediat

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