10 citations · 28 across the 8 of their papers we have counts for
9 papers
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…
PASTA: Pretrained Action-State Transformer Agents
Raphael Boige, Yannis Flet-Berliac, Arthur Flajolet +2
Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology. Recent approaches involve pre-training t…
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…
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…
Fast Population-Based Reinforcement Learning on a Single Machine
Arthur Flajolet, Claire Bizon Monroc, Karim Beguir +1
Training populations of agents has demonstrated great promise in Reinforcement Learning for stabilizing training, improving exploration and asymptotic performance, and generating a…
Logarithmic regret bounds for Bandits with Knapsacks
Arthur Flajolet, Patrick Jaillet
Optimal regret bounds for Multi-Armed Bandit problems are now well documented. They can be classified into two categories based on the growth rate with respect to the time horizon…
No-Regret Learnability for Piecewise Linear Losses
Arthur Flajolet, Patrick Jaillet
In the convex optimization approach to online regret minimization, many methods have been developed to guarantee a bound on regret for subdifferentiable convex loss f…
Robust Adaptive Routing Under Uncertainty
Arthur Flajolet, Sebastien Blandin, Patrick Jaillet
We consider the problem of finding an optimal history-dependent routing strategy on a directed graph weighted by stochastic arc costs when the objective is to minimize the risk of…