Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets. However, the challenge of exerting control over the generation process of GANs remains a significant hurdle. In this paper, we propose a novel methodology to address this issue by integrating a reinforcement learning (RL) agent with a latent-space GAN (l-GAN), thereby facilitating the generation of desired outputs. More specifically, we have developed an actor-critic RL agent with a meticulously designed reward policy, enabling it to acquire proficiency in navigating the latent space of the l-GAN and generating outputs based on specified tasks. To substantiate the efficacy of our approach, we have conducted a series of experiments employing the MNIST dataset, including arithmetic addition as an illustrative task. The outcomes of these experiments serve to validate our methodology. Our pioneering integration of an RL agent with a GAN model represents a novel advancement, holding great potential for enhancing generative networks in the future.

翻译：生成对抗网络（GAN）已成为基于训练数据集生成逼真输出的强大人工智能工具。然而，如何有效控制GAN的生成过程仍是重大挑战。本文提出一种新颖方法，通过将强化学习（RL）智能体与潜在空间GAN（l-GAN）相结合，实现目标输出的定向生成。具体而言，我们开发了具有精心设计奖励策略的演员-评论家RL智能体，使其能够掌握在l-GAN潜在空间中导航的技能，并根据指定任务生成输出。为验证该方法有效性，我们采用MNIST数据集开展系列实验，其中包含将算术加法作为示例性任务。实验结果证实了该方法的有效性。首次将RL智能体与GAN模型相融合的创新方案，为未来增强生成网络提供了重要潜力。