Chongyi Zheng will present his General Exam "Towards Unsupervised Pre-training for Reinforcement Learning" on Wednesday, January 14, 2026 at 11:00 AM in CS 301 and via zoom.

Zoom link: https://princeton.zoom.us/j/93974458862?pwd=1OO9PebfnbDu9vPe6XmmaP5TukAS7G.1

Committee Members: Benjamin Eysenbach (advisor), Peter Henderson, Karthik Narasimhan

Abstract:

Unsupervised reinforcement learning (RL) promises to solve new tasks without additional environment interaction, by leveraging prior experience and powerful representations. However, when and why unsupervised RL works remains poorly understood. In this presentation, I will explore one framework that enables unsupervised pre-training in RL, focusing on how temporal representations, training objectives, and policy adaptation jointly shape an agent’s ability to solve previously unseen tasks. I will propose new ways to understand a prior state-of-the-art unsupervised RL algorithm and develop a simplified version. I will also discuss some theoretical tools and diagnostic experiments that help us probe information acquired by unsupervised RL agents.

Reading List:

https://docs.google.com/document/d/1kEEdCLEyvFqUQ4bFDP-go0sBWNGIh7YBi2rwpSEXIwA/edit?usp=sharing

Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.