13 citations · 58 across the 14 of their papers we have counts for
4 papers · 1 filter
Discovering Unsupervised Behaviours from Full-State Trajectories
Luca Grillotti, Antoine Cully
Improving open-ended learning capabilities is a promising approach to enable robots to face the unbounded complexity of the real-world. Among existing methods, the ability of Quali…
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…
Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms
Luca Grillotti, Antoine Cully
Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving do…
Accelerated Quality-Diversity through Massive Parallelism
Bryan Lim, Maxime Allard, Luca Grillotti +1
Quality-Diversity (QD) optimization algorithms are a well-known approach to generate large collections of diverse and high-quality solutions. However, derived from evolutionary com…