Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929237478342656 |
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| author | Franceschelli, Giorgio Musolesi, Mirco |
| author_facet | Franceschelli, Giorgio Musolesi, Mirco |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_00031 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges Franceschelli, Giorgio Musolesi, Mirco Machine Learning Artificial Intelligence 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. |
| title | Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2308.00031 |