Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

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

Felix Chalumeau
  • Fonction : Auteur
Thomas Pierrot
  • Fonction : Auteur
Valentin Macé
  • Fonction : Auteur
Arthur Flajolet
  • Fonction : Auteur
Karim Beguir
  • Fonction : Auteur

Résumé

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 algorithms inspired by this observation, that obtained great results in many applications, from wing design to robot adaptation. Recently, several works demonstrated that these methods could be applied to perform neuro-evolution to solve control problems in large search spaces. In such problems, diversity can be a target in itself. Diversity can also be a way to enhance exploration in tasks exhibiting deceptive reward signals. While the first aspect has been studied in depth in the QD community, the latter remains scarcer in the literature. 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. Therefore, we believe that standardized benchmarks exhibiting control problems in high dimension with exploration difficulties are of interest to the QD community. In this paper, we highlight three candidate benchmarks and explain why they appear relevant for systematic evaluation of QD algorithms. We also provide open-source implementations in Jax allowing practitioners to run fast and numerous experiments on few compute resources.

Dates et versions

hal-04237990 , version 1 (11-10-2023)

Identifiants

Citer

Felix Chalumeau, Thomas Pierrot, Valentin Macé, Arthur Flajolet, Karim Beguir, et al.. Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems. GECCO 2022 Workshop on Quality Diversity Algorithm Benchmarks, Jul 2022, Boston (MA), United States. ⟨hal-04237990⟩
20 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More