10.1k citations · 13.1k across the 22 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…
Meta-Learning by the Baldwin Effect
Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero +6
The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstr…
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols +3
In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations duri…
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…