40 citations · 130 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Evolving Populations of Diverse RL Agents with MAP-Elites
Thomas Pierrot, Arthur Flajolet
Quality Diversity (QD) has emerged as a powerful alternative optimization paradigm that aims at generating large and diverse collections of solutions, notably with its flagship alg…
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding Exploration
Hannah Janmohamed, Thomas Pierrot, Antoine Cully
Quality-Diversity (QD) algorithms have recently gained traction as optimisation methods due to their effectiveness at escaping local optima and capability of generating wide-rangin…
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…
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…