## 4 Results and Discussion
### RQ1: What NAS Approaches Are Applied to GANs in the Literature?
#### 4.1.2 Reinforcement Learning Approaches  
RL is a paradigm of machine learning inspired by behavioral psychology, where an agent learns to make decisions by interacting with an environment [33]. This approach enables systems to learn optimal policies in complex, dynamic settings by maximizing cumulative rewards, making it well-suited for solving sequential decision-making problems in searching for neural network architecture. RL approaches can be categorized as on-policy and off-policy approaches. * On-Policy Search  
[PERSON] et al. (2019) [12] introduced AutoGAN, a novel method that uses a Recurrent Neural Network (RNN) controller to guide the search process for generator architectures. AutoGAN employs a multi-level search strategy with beam search, using SoftMax predictions for sampling architecture variations. While it focuses solely on optimizing the generator, with the discriminator growing in a pre-defined manner, AutoGAN demonstrates the potential of using IS as a reward signal in RL for GAN design, in just 2 GPU Days. However, this approach is limited by its lack of discriminator architecture search and potential scalability issues for higher resolution images. [PERSON] et al. (2019) [34] developed AGAN, a reinforcement learning framework that simultaneously searches both generator and discriminator architectures. AGAN employs a two-layer LSTM controller RNN and uses policy gradient with REINFORCE, incorporating an entropy bonus for exploration. This approach stands out by allowing for arbitrary cell topologies and demonstrating adaptability to different image sizes and complexities. AGAN uses a shaped reward function based on IS, potentially offering more nuanced guidance for architecture optimization. However, the simultaneous search of both generator and discriminator architectures lead to increased computational requirements as it takes 1200 GPU Days and necessitates careful tuning of the reward function shaping. [PERSON] et al. (2020) [35] proposed Multi-Net NAS (MN-NAS), a novel approach that leverages reinforcement learning to design class-aware generators for conditional GANs (cGANs). MN-NAS employs an MDP with a moving average mechanism to sample and evaluate candidate architectures. The key innovation lies in its ability to search for distinct generator architectures for each class within a single search procedure, addressing the challenge of combinatorial explosion as the number of classes increases. The search space includes regular convolutions and class-modulated convolutions (CMconv), which allow for the sharing of training data across different architectures, mitigating the issue of insufficient data per class. MN-NAS also introduces mixed-architecture optimization, enabling efficient parallelization of the search and re-training processes. The method demonstrates competitive performance on CIFAR10 and CIFAR100. However, the approach is limited by its focus on generator architecture search, with the discriminator following a predefined design, and the potential scalability issues when applied to datasets with a very large number of classes. * Off-Policy Search  
[PERSON] et al. (2020) [36] proposed E\({}^{2}\)GAN, an off-policy reinforcement learning framework that reformulates the search problem as a Markov Decision Process (MDP). E\({}^{2}\)GAN utilizes a soft actor-critic algorithm and a progressive state representation, significantly improving sample efficiency. The framework implements exploration and exploitation periods, focusing on cell architectures. E\({}^{2}\)GAN's key innovation lies in its ability to discover competitive GAN architectures in just 7 GPU Hours, using a combined reward function of IS and FID. While E\({}^{2}\)GAN offers rapid architecture discovery, a limitation of this approach is that it only searches for the generator. [PERSON] et al. (2022) [37] introduced T2 IGAN the first work to apply NAS principles for designing GANs that integrate transformer modules into the text-to-image synthesis framework.