1.3k citations · 6.3k across the 48 of their papers we have counts for
4 papers · 1 filter
Fast deep reinforcement learning using online adjustments from the past
Steven Hansen, Pablo Sprechmann, Alexander Pritzel +2
We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer. EVA shifts the value pred…
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson +4
Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks…
Pushing the bounds of dropout
Gábor Melis, Charles Blundell, Tomáš Kočiský +3
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the o…
Memory-based Parameter Adaptation
Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae +7
Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as the…