* denotes an equal contribution

denotes a spotlight paper.

denotes a paper receiving an award.

• Deep Double Q-learning
  Prabhat Nagarajan, Martha White, and Marlos C. Machado
  Preprint. June 2025.
  arXiv
  
  

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• Accelerating Q-learning through Efficient Value-Sharing across Actions
  Prabhat Nagarajan, Brett Daley, Martha White and Marlos C. Machado
  International Conference on Machine Learning (ICML), Spotlight, July 2026.
    Best Paper Runner-up at Adaptive and Learning Agents Workshop at AAMAS 2026
  
  
• When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
  Vincent Liu, Prabhat Nagarajan, Andrew Patterson, and Martha White
  International Conference on Autonomous Agents and Multiagent Systems (AAMAS). May 2026.
  arXiv BibTeX
  
  

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• An Analysis of Action-Value Temporal-Difference Methods That Learn State Values
  Brett Daley*, Prabhat Nagarajan*, Martha White, and Marlos C. Machado
  Reinforcement Learning Journal (RLJ). August 2025.
  PDF arXiv BibTeX Code
  
  

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• Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning
  Zhang-Wei Hong, Prabhat Nagarajan, and Guilherme J. Maeda
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). September 2021.
  PDF BibTeX Code
  
  
• Reconnaissance for Reinforcement Learning with Safety Constraints
  Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintaro Okada, Masanori Koyama, and Prabhat Nagarajan
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). September 2021.
  PDF BibTeX
  
  
• ChainerRL: A Deep Reinforcement Learning Library
  Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, and Takahiro Ishikawa
  Journal of Machine Learning Research (JMLR). 22(77):1−14, April 2021.
  JMLR page PDF arXiv BibTeX
  
  

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• Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators
  Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu, Prabhat Nagarajan, Shimpei Masuda, and Mario Ynocente Castro
  International Conference on Intelligent Robots and Systems (IROS) October 2020.
  PDF arXiv BibTeX Video
  
  

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• Learning Latent State Spaces for Planning through Reward Prediction
  Aaron Havens, Yi Ouyang, Prabhat Nagarajan, and Yasuhiro Fujita
  Workshop on Deep Reinforcement Learning at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) December 2019.
  PDF BibTeX arXiv


• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
  Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum
  International Conference on Machine Learning (ICML) June 2019.
  PDF BibTeX arXiv Code


• Deterministic Implementations for Reproducibility in Deep Reinforcement Learning
  Prabhat Nagarajan, Garrett Warnell, and Peter Stone
  AAAI 2019 Workshop on Reproducible AI. January 2019.
  PDF BibTeX arXiv Code

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• Nondeterminism as a Reproducibility Challenge for Deep Reinforcement Learning
  Prabhat Nagarajan
  Master's Thesis, The University of Texas at Austin, August 2018
  Committee: Peter Stone (Supervisor), Scott Niekum
  PDF BibTeX


• The Impact of Nondeterminism on Reproducibility in Deep Reinforcement Learning
  Prabhat Nagarajan, Garrett Warnell, and Peter Stone
  2nd Reproducibility in Machine Learning Workshop at ICML 2018, Stockholm, Sweden.
  PDF BibTeX Code

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