9 citations · 19 across the 10 of their papers we have counts for
4 papers · 1 filter
Exploration by Running Away from the Past
Paul-Antoine Le Tolguenec, Yann Besse, Florent Teichteil-Koenigsbuch +2
The ability to explore efficiently and effectively is a central challenge of reinforcement learning. In this work, we consider exploration through the lens of information theory. S…
On Neural Consolidation for Transfer in Reinforcement Learning
Valentin Guillet, Dennis G. Wilson, Carlos Aguilar-Melchor +1
Although transfer learning is considered to be a milestone in deep reinforcement learning, the mechanisms behind it are still poorly understood. In particular, predicting if knowle…
When, where, and how to add new neurons to ANNs
Kaitlin Maile, Emmanuel Rachelson, Hervé Luga +1
Neurogenesis in ANNs is an understudied and difficult problem, even compared to other forms of structural learning like pruning. By decomposing it into triggers and initializations…
Disentanglement by Cyclic Reconstruction
David Bertoin, Emmanuel Rachelson
Deep neural networks have demonstrated their ability to automatically extract meaningful features from data. However, in supervised learning, information specific to the dataset us…