Deterministic Implementations for Reproducibility in Deep Reinforcement Learning. - Prabhat Nagarajan; Garrett Warnell; Peter Stone 001 (CoRR, 2018)

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. - Daniel S. Brown; Wonjoon Goo; Prabhat Nagarajan; Scott Niekum (ICML, 2019)

Learning Latent State Spaces for Planning through Reward Prediction. - Aaron J. Havens; Yi Ouyang; Prabhat Nagarajan; Yasuhiro Fujita 001 (CoRR, 2019)

Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators. - Yasuhiro Fujita 001; Kota Uenishi; Avinash Ummadisingu; Prabhat Nagarajan; Shimpei Masuda; Mario Ynocente Castro (IROS, 2020)

Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning. - Zhang-Wei Hong; Prabhat Nagarajan; Guilherme Maeda (ECML/PKDD (1), 2021)

Reconnaissance for Reinforcement Learning with Safety Constraints. - Shin-ichi Maeda; Hayato Watahiki; Yi Ouyang; Shintarou Okada; Masanori Koyama; Prabhat Nagarajan (ECML/PKDD (2), 2021)

ChainerRL: A Deep Reinforcement Learning Library. - Yasuhiro Fujita 001; Prabhat Nagarajan; Toshiki Kataoka; Takahiro Ishikawa (J. Mach. Learn. Res., 2021)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning? - Vincent Liu; Prabhat Nagarajan; Andrew Patterson; Martha White (CoRR, 2023)

Double Q-learning for Value-based Deep Reinforcement Learning, Revisited. - Prabhat Nagarajan; Martha White; Marlos C. Machado (CoRR, 2025)

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values. - Brett Daley; Prabhat Nagarajan; Martha White; Marlos C. Machado (CoRR, 2025)