8 citations · 8 across the 3 of their papers we have counts for
3 papers
Efficient Exploration using Model-Based Quality-Diversity with Gradients
Bryan Lim, Manon Flageat, Antoine Cully
Exploration is a key challenge in Reinforcement Learning, especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches h…
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…