activity
20202023
most citedDiversity Policy Gradient for Sample Efficient Quality-Diversity Optimization

40 citations · 63 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2023★ 10 cited

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration

Felix Chalumeau, Bryan Lim, Raphael Boige +7

QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization…

cs.NE2023★ 5 cited

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

Valentin Macé, Raphaël Boige, Felix Chalumeau +3

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 b…

cs.NE2022★ 2 cited

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

Felix Chalumeau, Thomas Pierrot, Valentin Macé +4

A fascinating aspect of nature lies in its ability to produce a collection of organisms that are all high-performing in their niche. Quality-Diversity (QD) methods are evolutionary…

cs.NE2022★ 6 cited

Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery

Felix Chalumeau, Raphael Boige, Bryan Lim +5

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for training neural policies to solve complex control tasks. However, these policies tend to be overfit to the e…

cs.AI2020★ 40 cited

Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization

Thomas Pierrot, Valentin Macé, Félix Chalumeau +6

A fascinating aspect of nature lies in its ability to produce a large and diverse collection of organisms that are all high-performing in their niche. By contrast, most AI algorith…