Prabhat Nagarajan

According to our database, Prabhat Nagarajan authored at least 10 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values.

Brett Daley, Prabhat Nagarajan, Martha White, Marlos C. Machado

CoRR, July, 2025

Double Q-learning for Value-based Deep Reinforcement Learning, Revisited.

Prabhat Nagarajan, Martha White, Marlos C. Machado

CoRR, July, 2025

2023

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?

Vincent Liu, Prabhat Nagarajan, Andrew Patterson, Martha White

CoRR, 2023

2021

ChainerRL: A Deep Reinforcement Learning Library.

Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa

J. Mach. Learn. Res., 2021

Reconnaissance for Reinforcement Learning with Safety Constraints.

Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintarou Okada, Masanori Koyama, Prabhat Nagarajan

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning.

Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

2020

Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators.

Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu, Prabhat Nagarajan, Shimpei Masuda, Mario Ynocente Castro

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

2019

Learning Latent State Spaces for Planning through Reward Prediction.

Aaron J. Havens, Yi Ouyang, Prabhat Nagarajan, Yasuhiro Fujita

CoRR, 2019

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations.

Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Deterministic Implementations for Reproducibility in Deep Reinforcement Learning.

Prabhat Nagarajan, Garrett Warnell, Peter Stone

CoRR, 2018