Welcome!

I am a third-year Computer Science PhD candidate at the University of Wisconsin-Madison.

I am broadly interested in representation learning and abstractions for reinforcement learning. Poorly learned representations can lead to data-inefficient learning, instability, and high variance. My works studies how we can design representation learning objectives that can enable an RL agent to reliably make predictions about its environment for validation and control.

I am fortunate to be advised by Josiah Hanna, and I also closely collaborate with Qiaomin Xie and Yudong Chen. I have also interned at Sony AI.

Previously, I completed my BS and MS in Computer Science from the University of Texas at Austin, where I was fortunate to be advised by Peter Stone. I also worked as a software engineer at Salesforce and SAS Institute.

Feel free to shoot me an email if you want to chat!

News

  • May 2024: Our paper, Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces, was accepted at ICML 2024!
  • November 2023: I won the Top Reviewer Award at NeurIPS 2023!
  • September 2023: Our paper, State-Action Similarity-Based Representations for Off-Policy Evaluation, was accepted at NeurIPS 2023!
  • May 2023: I will be working on the RL team at Sony AI this summer!
  • November 2022: Our paper, Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction, was accepted at AAAI 2023!
  • May 2022: I received the CS Summer Research Fellowship!

Pre-prints


Publications

Conference Papers

2024

Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces

[arxiv] [code]
Brahma S. Pavse, Matthew Zurek, Yudong Chen, Qiaomin Xie, Josiah P. Hanna
Proceedings of the 41st International Conference on Machine Learning (ICML), July 2024.  

2023

State-Action Similarity-Based Representations for Off-Policy Evaluation

[arxiv] [bibtex] [code]
Brahma S. Pavse, Josiah P. Hanna
Proceedings of the 36th Neural Information Processing Systems (NeurIPS), December 2023.  

Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction (Oral Presentation)

[pdf] [bibtex]
Brahma S. Pavse, Josiah P. Hanna
Proceedings of the 37th Association for the Advancement of Artificial Intelligence (AAAI), February 2023.  
An earlier version appeared at the Offline RL Workshop: Offline RL as a "Launchpad" at NeurIPS 2022.  

2020

Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration

[pdf] [bibtex]
Brahma S. Pavse*, Faraz Torabi*, Josiah Hanna, Garrett Warnell, Peter Stone
*Equal contribution.
Contains material from my undergraduate honors thesis.
IEEE Robotics and Automation Letters, July 2020.  
Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), October 2020.  
An earlier version appeared in the Imitation, Intent, and Interaction (I3) workshop at ICML 2019.  

Journal Articles

2020

Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration

[pdf] [bibtex]
Brahma S. Pavse*, Faraz Torabi*, Josiah Hanna, Garrett Warnell, Peter Stone
*Equal contribution.
Contains material from my undergraduate honors thesis.
IEEE Robotics and Automation Letters, July 2020.  
Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), October 2020.  
An earlier version appeared in the Imitation, Intent, and Interaction (I3) workshop at ICML 2019.  

Theses

Reducing Sampling Error in Batch Temporal Difference Learning

[pdf] [bibtex]
Brahma S. Pavse, advised by Peter Stone and Josiah Hanna
MS Thesis, University of Texas at Austin, 2020.  

Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration

[pdf] [bibtex]
Brahma S. Pavse, advised by Peter Stone
BS Honors Thesis, University of Texas at Austin, 2019.  

Awards and Honors

  • NeurIPS Top Reviewer Award (2023).
  • UW Madison CS Summer Research Fellowship (2022).
  • UW Madison CS Graduate Scholarship (2022).
  • UT Austin University Honors (2020).
  • UT Austin CS Special Departmental Honors (Research) (2020).
  • RoboCup 3D Simulation League World Champions (2019).
  • Eva Stevenson Woods Endowed Presidential Scholarship (2019).
  • National Instruments Endowed Scholarship (2019).
  • RoboCup 3D Simulation League World Champions (2018).

Service

  • Senior Reviewer, Reinforcement Learning Conference (RLC) (2024).
  • Program Committee Member, Association for the Advancement of Artificial Intelligence (AAAI) (2023).
  • Graduate Student Mentor, Wisconsin Science and Computing Emerging Research Stars (WISCERS) (2022).
  • Reviewer, Neural Information Processing Systems (NeurIPS) (2023, 2022).
  • Reviewer, International Conference on Learning Representations (ICLR) (2023, 2022).
  • Reviewer, International Conference on Robotics and Automation (ICRA) (2021).
  • Reviewer, UT Austin Computer Science Dept. MS Admissions Committee (2020).