12 citations · 21 across the 6 of their papers we have counts for
3 papers · 1 filter
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
Manon Flageat, Bryan Lim, Luca Grillotti +3
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments…
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity
Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis +2
In real-world environments, robots need to be resilient to damages and robust to unforeseen scenarios. Quality-Diversity (QD) algorithms have been successfully used to make robots…
Hierarchical Quality-Diversity for Online Damage Recovery
Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis +1
Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-train…