Generative Sequence Models for Sequential Decision Making
Offered By: VinAI via YouTube
Course Description
Overview
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.
Syllabus
Seminar Series: Generative Sequence Models for Sequential Decision Making
Taught by
VinAI
Related Courses
Toward Generalizable Embodied AI for Machine AutonomyBolei Zhou via YouTube What Are the Statistical Limits of Offline Reinforcement Learning With Function Approximation?
Simons Institute via YouTube Better Learning from the Past - Counterfactual - Batch RL
Simons Institute via YouTube Off-Policy Policy Optimization
Simons Institute via YouTube Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin
Institute for Advanced Study via YouTube