Generative Models for Decision Making

Bogdan Mazoure, Lisa Lee, Roberta Raileanu, Yilun Du, Walter Talbott, Katherine Metcalf, R Devon Hjelm, Alexander T Toshev

Workshop Type: In-person

Keywords: generative models, decision making, foundation models, large language models, diffusion, exploration, generalization

Abstract:

Generative Artificial Intelligence (AI) has made significant advancements in recent years, particularly with the development of large language and diffusion models. These generative models have demonstrated impressive capabilities in various tasks, such as text generation and image and audio synthesis. Concurrently, Reinforcement Learning (RL) has made significant strides in solving complex sequential decision-making problems with the help of external knowledge sources . However, there remains untapped potential in combining generative models with RL algorithms to tackle real-world challenges, particularly to improve sample efficiency of tabula rasa training by introducing priors from related domains such as visual question-answering, image captioning and image generation.
This workshop aims to bring together researchers and practitioners from the fields of generative AI and reinforcement learning to explore the latest advances, methodologies, and applications. By fostering collaborations between these two domains, we intend to unlock new opportunities for addressing complex problems that lie at the intersection of both fields.

Submission Number: 17