Prabhat Nagarajan

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 12

Top Topics

Reinforcement Learning

Temporal Difference Learning

Function Approximation

Partially Observable Environments

Top Venues

J. Mach. Learn. Res.

Publications

Vincent Liu, Prabhat Nagarajan, Andrew Patterson, Martha White
When is Offline Policy Selection Sample Efficient for Reinforcement Learning? CoRR (2023)
Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning. ECML/PKDD (1) (2021)
Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa
ChainerRL: A Deep Reinforcement Learning Library. J. Mach. Learn. Res. 22 (2021)
Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintarou Okada, Masanori Koyama, Prabhat Nagarajan
Reconnaissance for Reinforcement Learning with Safety Constraints. ECML/PKDD (2) (2021)
Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu, Prabhat Nagarajan, Shimpei Masuda, Mario Ynocente Castro
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators. CoRR (2020)
Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu, Prabhat Nagarajan, Shimpei Masuda, Mario Ynocente Castro
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators. IROS (2020)
Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning. CoRR (2020)
Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, Takahiro Ishikawa
ChainerRL: A Deep Reinforcement Learning Library. CoRR (2019)
Aaron J. Havens, Yi Ouyang, Prabhat Nagarajan, Yasuhiro Fujita
Learning Latent State Spaces for Planning through Reward Prediction. CoRR (2019)
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. ICML (2019)
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. CoRR (2019)
Prabhat Nagarajan, Garrett Warnell, Peter Stone
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning. CoRR (2018)