Welcome!

I am a first-year Computer Science PhD candidate at the University of Wisconsin-Madison, where I am fortunate to be advised by Josiah Hanna. My research interests are in reinforcement learning (RL). Thus far, I have studied imitation learning and off-policy RL problems.

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

  • January 2022: I joined UW-Madison to start my PhD!
  • October 2020: Our paper, On Sampling Error in Batch Action-Value Prediction Algorithms, was accepted at the Offline RL workshop at NeurIPS 2020!
  • July 2020: Our paper, Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration, was accepted at IROS 2020 and the IEEE Robotics and Automation Letters!
  • June 2020: Our paper, Reducing Sampling Error in Batch Temporal Difference Learning, was accepted at ICML 2020!

Publications

Conference Papers

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

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
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

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.  

Awards and Honors

  • UW Madison CS Graduate Scholarship (2022).
  • UT Austin University Honors (2020).
  • UT Austin CS Special Departmental Honors (Research) (2020).
  • UT Austin + Bosch Summer Research Funding (2020).
  • RoboCup 3D Simulation League World Champions (2019).
  • RoboCup 3D Simulation Technical Challenge World Champions (2019).
  • Eva Stevenson Woods Endowed Presidential Scholarship (2019).
  • National Instruments Endowed Scholarship (2019).
  • RoboCup 3D Simulation League World Champions (2018).
  • RoboCup 3D Simulation Technical Challenge 3rd Place (2018).
  • RoboCup 3D Simulation Asia Pacific Champions (2018).
  • UT Austin College Scholar (2015-2019).

Service

  • Graduate Student Mentor, Wisconsin Science and Computing Emerging Research Stars (WISCERS) (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).