Explore the intersection of generative sequence models and sequential decision making in this seminar presented by Aditya Grover, Assistant Professor of Computer Science at UCLA. Delve into a framework that abstracts sequential decision making as a generative sequence modeling problem, leveraging the Transformer architecture and advances in language modeling. Learn how this approach enables learning from large offline datasets, uncertainty-guided online exploration, and generalization across multiple tasks. Discover how the framework performs on various benchmarks, from continuous control to game playing, matching or exceeding state-of-the-art algorithms. Gain insights into efficient machine learning approaches for probabilistic reasoning under limited supervision, with applications in climate science and sustainable energy.