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。本综述探讨了将强化学习应用于生成式人工智能的前沿进展、机遇与开放研究问题。具体而言，我们将讨论三类应用：强化学习作为无需明确目标的替代生成方式；作为在生成输出同时最大化目标函数的方式；以及作为将难以通过目标函数捕捉的期望特性嵌入生成过程的方式。最后，我们深入探讨了这一引人入胜的新兴领域中的机遇与挑战，以此作为综述的总结。