Join us on June 9, when Guest Lecturer Amy Zhang will hold a Guest Lecture on Generative Models in Reinforcement Learning and Behavioral Foundation Models! The lecture is part of Guillaume Bellec’s Tatjana Chavdarova’s, and Thomas Gärtner’s Reinforcement Learning course.

Generative Models in Reinforcement Learning and Behavioral Foundation Models

We explore the intersection of generative AI and reinforcement learning, demonstrating some ways generative models can be leveraged in RL. I also introduce some forms of self-supervised reinforcement learning algorithms that can be used to learn behavioral foundation models using the successor measure. Through a self-supervised pre-training phase, we can zero-shot extract a policy for any downstream task. We then show that many self-supervised RL methods can be unified through the successor measure, providing insights on future research directions.

Join us Online

The lecture will be streamed via Zoom (Meeting ID: 687 5180 8862, Password: B6s0Vtm8)

About Amy Zhang

Amy Zhang is an Assistant Professor at the UT Austin in the Chandra Family Department of Electrical and Computer Engineering. Her work focuses on improving generalization in reinforcement learning through bridging theory and practice in learning and utilizing structure in real-world problems. Previously, she was a Research Scientist at Meta AI FAIR and a postdoctoral fellow at University of California, Berkeley. She obtained her PhD from McGill University and the Mila Institute, and previously earned an MEng in EECS and dual BSc degrees in Mathematics and EECS from MIT.

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