I design reinforcement learning (RL) algorithms: AI methods that learn how to make intelligent decisions from trial and error. I am especially interested in self-supervised methods, which enable agents to learn intelligent behaviors without labels or human supervision. Our group has developed some of the foremost algorithms and analysis for such self-supervised RL methods. Hereare a few examples. I run the Princeton Reinforcement Learning Lab.
Bio: Before joining Princeton, I did by PhD in machine learning at CMU under Ruslan Salakhutdinov and Sergey Levine and supported by the NSF GFRP and the Hertz Fellowship. I spent a number of years at Google Brain/Research before and during my PhD. My undergraduate studies were in math at MIT.
Join us! (Fall 2024) I am recruiting 1 – 2 PhD students and 1 – 2 MS students to start in Fall 2025. I am also recruiting 1 postdoc and 1 predoc researcher to start in Spring 2025. Please read this page for more information and details on how to apply.
news
Feb 1, 2025
I’m giving an invited talk for the “Control, Optimization, and Reinforcement Learning Session” at the Coordinated Science Laboratory Student Conference in UIUC in February 24 – 26, 2025. Email me to meet up if you’ll be there!
Awarded a grant from the Princeton AI Lab to study ``Do brains perceive, act, and plan using temporal contrast?’’ together with Nathaniel Daw.
Jan 2, 2025
We’re launching a undergraduate research program (REU) together with state and community colleges in NJ. This is a paid program, and no research experience is required. Apply by Feb. 1.
The aim is to highlight a small subset of the work done in the group, and to give a sense for the sorts of problems that we're working on. Please see Google Scholar for a complete and up-to-date list of publications.
2024
Learning to Assist Humans without Inferring Rewards
Vivek Myers, Evan Ellis, Sergey Levine, Benjamin Eysenbach, and Anca Dragan
In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024
I’m giving an invited talk for the “Control, Optimization, and Reinforcement Learning Session” at the Coordinated Science Laboratory Student Conference in UIUC in February 24 – 26, 2025. Email me to meet up if you’ll be there! 
Check out our new preprint, Horizon Generalization in Reinforcement Learning, led by Cathy Ji and Vivek Myers! 
Awarded a grant from the Princeton AI Lab to study ``Do brains perceive, act, and plan using temporal contrast?’’ together with Nathaniel Daw. 
We’re launching a undergraduate research program (REU) together with state and community colleges in NJ. This is a paid program, and no research experience is required. Apply by Feb. 1. 
I’m co-organizing the NSF workshop on RL, which will be held Jan 23 – 24 at Harvard University, together with Na Li, Guannan Qu and Bo Dai! Register here. 
A Connection between One-Step RL and Critic Regularization in Reinforcement LearningIn International Conference on Machine Learning, 2023
Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get TherePhD Thesis, Carnegie Mellon University, 2023
