Benjamin Eysenbach
Assistant Professor of Computer Science at Princeton University.
Affiliated/Associated Faculty with the Princeton Program in Cognitive Science and the Princeton Language Initiative.
Room 416
35 Olden St
Princeton NJ 08544
eysenbach@princeton.eduI 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. Here are 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! Please read this page before emailing me about joining the lab.
news
| Aug 1, 2025 |  I’ll be giving a few lectures on RL at the Machine Learning Summer School. Looking forward to spending time with the students and faculty there! |
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| Apr 24, 2025 |  Princeton RL @ ICLR 2025! Some say hi in Singapore!
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| 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! |
| Feb 1, 2025 |  Check out our new preprint, Horizon Generalization in Reinforcement Learning, led by Cathy Ji and Vivek Myers! |
| Jan 7, 2025 |  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. |
| Jan 2, 2025 |  I’m teaching Introduction to Reinforcement Learning this Spring, together with a fantastic team of TAs. I create this course to give students a strong foundation in RL and highlight that unifying themes (RL isn’t just a bag of tricks). All course notes and assignments will be posted publicly, so you can follow along! |
| Jan 1, 2025 |  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. |
| Nov 22, 2024 | Princeton RL @ NeurIPS 2024! Some say ``hi’’ and check out the work below!
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| Sep 13, 2024 |  JaxGCRL: A new benchmark for goal-conditioned RL is blazing fast, allowing you to train at 1 million steps per minute on 1 GPU. Experiments run so fast that the algorithm design process becomes interactive. Tools like this not only make research much more accessible (e.g., you can now run a bunch of interesting experiments in a free Colab notebook before the 90 min timeout), but also will change how RL is taught (less fighting with dependencies, more experiments on complex tasks, less waiting for experiments to queue and finish); stay tuned for COS 435 this Spring! |
selected publications
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
2023
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A Connection between One-Step RL and Critic Regularization in Reinforcement LearningIn International Conference on Machine Learning, 2023 -
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Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get TherePhD Thesis, Carnegie Mellon University, 2023
