71 citations · 141 across the 9 of their papers we have counts for
10 papers
Accelerating Q-learning through Efficient Value-Sharing across Actions
Prabhat Nagarajan, Brett Daley, Martha White +1
Action values are foundational to many control algorithms such as Q-learning. Therefore, efficient action-value learning is central to reinforcement learning (RL). However, learnin…
An Analysis of Action-Value Temporal-Difference Methods That Learn State Values
Brett Daley, Prabhat Nagarajan, Martha White +1
The hallmark feature of temporal-difference (TD) learning is bootstrapping: using value predictions to generate new value predictions. The vast majority of TD methods for control l…
Deep Double Q-learning
Prabhat Nagarajan, Martha White, Marlos C. Machado
Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q-learning. To do so, it explicitly trains two independent action-value functions and use…
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
Vincent Liu, Prabhat Nagarajan, Andrew Patterson +1
Offline reinforcement learning algorithms often require careful hyperparameter tuning. Before deployment, we need to select amongst a set of candidate policies. However, there is l…
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators
Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu +3
Developing personal robots that can perform a diverse range of manipulation tasks in unstructured environments necessitates solving several challenges for robotic grasping systems.…
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning
Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
Off-policy ensemble reinforcement learning (RL) methods have demonstrated impressive results across a range of RL benchmark tasks. Recent works suggest that directly imitating expe…