Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges

• URL: http://arxiv.org/abs/2308.00031v4
• Date: Thu, 8 Feb 2024 12:48:23 GMT
• Title: Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
• Authors: Giorgio Franceschelli and Mirco Musolesi
• Abstract summary: Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. We will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area.
• Score: 2.9008806248012333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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