9 citations · 19 across the 10 of their papers we have counts for
10 papers
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…
Exploration by Learning Diverse Skills through Successor State Measures
Paul-Antoine Le Tolguenec, Yann Besse, Florent Teichteil-Konigsbuch +2
The ability to perform different skills can encourage agents to explore. In this work, we aim to construct a set of diverse skills which uniformly cover the state space. We propose…
Genetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies
Paul Templier, Emmanuel Rachelson, Antoine Cully +1
Evolutionary Algorithms (EA) have been successfully used for the optimization of neural networks for policy search, but they still remain sample inefficient and underperforming in…
Quality with Just Enough Diversity in Evolutionary Policy Search
Paul Templier, Luca Grillotti, Emmanuel Rachelson +2
Evolution Strategies (ES) are effective gradient-free optimization methods that can be competitive with gradient-based approaches for policy search. ES only rely on the total episo…
Dynamic unsplittable flows with path-change penalties: new formulations and solution schemes for large instances
François Lamothe, Emmanuel Rachelson, Alain Haït +2
In this work, we consider the dynamic unsplittable flow problem. This variation of the unsplittable flow problem has received little attention so far. The unsplittable flow problem…
Randomized rounding algorithms for large scale unsplittable flow problems
François Lamothe, Emmanuel Rachelson, Alain Haït +2
Unsplittable flow problems cover a wide range of telecommunication and transportation problems and their efficient resolution is key to a number of applications. In this work, we s…
Curiosity creates Diversity in Policy Search
Paul-Antoine Le Tolguenec, Emmanuel Rachelson, Yann Besse +1
When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search pr…
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…