21 citations · 47 across the 14 of their papers we have counts for
6 papers · 1 filter
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
Maxence Faldor, Félix Chalumeau, Manon Flageat +1
A hallmark of intelligence is the ability to exhibit a wide range of effective behaviors. Inspired by this principle, Quality-Diversity algorithms, such as MAP-Elites, are evolutio…
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Maxence Faldor, Félix Chalumeau, Manon Flageat +1
Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfu…
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…
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…
Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains
Manon Flageat, Felix Chalumeau, Antoine Cully
Quality-Diversity algorithms, among which MAP-Elites, have emerged as powerful alternatives to performance-only optimisation approaches as they enable generating collections of div…
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…