Search Results for author: Valentin Macé

Found 8 papers, 3 papers with code

Using Whole Document Context in Neural Machine Translation

no code implementations EMNLP (IWSLT) 2019 Valentin Macé, Christophe Servan

In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies.

Machine Translation Translation

Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization

1 code implementation NeurIPS 2021 Thomas Pierrot, Valentin Macé, Félix Chalumeau, Arthur Flajolet, Geoffrey Cideron, Karim Beguir, Antoine Cully, Olivier Sigaud, Nicolas Perrin-Gilbert

This paper proposes a novel algorithm, QDPG, which combines the strength of Policy Gradient algorithms and Quality Diversity approaches to produce a collection of diverse and high-performing neural policies in continuous control environments.

Continuous Control Evolutionary Algorithms

Sample efficient Quality Diversity for neural continuous control

no code implementations1 Jan 2021 Thomas Pierrot, Valentin Macé, Geoffrey Cideron, Nicolas Perrin, Karim Beguir, Olivier Sigaud

The QD part contributes structural biases by decoupling the search for diversity from the search for high return, resulting in efficient management of the exploration-exploitation trade-off.

Continuous Control Management +1

Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery

1 code implementation6 Oct 2022 Felix Chalumeau, Raphael Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot

Recent work has shown that training a mixture of policies, as opposed to a single one, that are driven to explore different regions of the state-action space can address this shortcoming by generating a diverse set of behaviors, referred to as skills, that can be collectively used to great effect in adaptation tasks or for hierarchical planning.

reinforcement-learning Reinforcement Learning (RL)

Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems

no code implementations24 Nov 2022 Felix Chalumeau, Thomas Pierrot, Valentin Macé, Arthur Flajolet, Karim Beguir, Antoine Cully, Nicolas Perrin-Gilbert

Exploration is at the heart of several domains trying to solve control problems such as Reinforcement Learning and QD methods are promising candidates to overcome the challenges associated.

Evolutionary Algorithms

The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers

no code implementations27 Mar 2023 Valentin Macé, Raphaël Boige, Felix Chalumeau, Thomas Pierrot, Guillaume Richard, Nicolas Perrin-Gilbert

In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space.

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