Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. In this survey, we discuss the state of the art, opportunities and open research questions in applying RL to generative AI. In particular, we will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives; as a way for generating outputs while concurrently maximizing an objective function; and, finally, as a way of embedding desired characteristics, which cannot be easily captured by means of an objective function, into the generative process. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area.

翻译：生成式人工智能是近十年来计算机科学领域最激动人心的发展之一。与此同时，强化学习已成为多种机器学习任务中非常成功的范式。在本综述中，我们探讨了将强化学习应用于生成式AI的最新进展、机遇与开放性研究问题。具体而言，我们将讨论三类应用：首先，将强化学习作为在无明确目标条件下进行生成的替代方案；其次，作为在生成输出同时最大化目标函数的手段；最后，作为将难以通过目标函数捕捉的期望特征嵌入生成过程的方法。我们以对这一新兴领域中机遇与挑战的深度讨论作为综述的结语。