Welcome!

I am a second-year Computer Science PhD candidate at the University of Wisconsin-Madison, where I am fortunate to be advised by Josiah Hanna. I am broadly interested in reinforcement learning (RL). Recently, I have been studying problems at the intersection of off-policy evaluation (OPE) and representation learning.

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 discuss anything!

News

  • 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!
  • January 2022: I joined UW-Madison to start my PhD!

Publications

Conference Papers

2023

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.  

Workshops and Symposia

2020

On Sampling Error in Batch Action-Value Prediction Algorithms

[pdf] [bibtex]
Brahma S. Pavse, Josiah Hanna, Ishan Durugkar, Peter Stone
Workshop on Offline Reinforcement Learning, Neural Information Processing Systems (NeurIPS), December 2020.  

2019

2018

Awards and Honors

  • 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

  • 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) (2022).
  • Reviewer, International Conference on Learning Representations (ICLR) (2022).
  • Reviewer, International Conference on Robotics and Automation (ICRA) (2021).
  • Reviewer, UT Austin Computer Science Dept. MS Admissions Committee (2020).