29 citations · 107 across the 28 of their papers we have counts for
3 papers · 1 filter
Faster Deep Reinforcement Learning with Slower Online Network
Kavosh Asadi, Rasool Fakoor, Omer Gottesman +3
Deep reinforcement learning algorithms often use two networks for value function optimization: an online network, and a target network that tracks the online network with some dela…
Flexible Model Aggregation for Quantile Regression
Rasool Fakoor, Taesup Kim, Jonas Mueller +2
Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being over…
Continuous Doubly Constrained Batch Reinforcement Learning
Rasool Fakoor, Jonas Mueller, Kavosh Asadi +2
Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive…