8 citations · 14 across the 4 of their papers we have counts for
4 papers
Quality with Just Enough Diversity in Evolutionary Policy Search
Paul Templier, Luca Grillotti, Emmanuel Rachelson +2
Evolution Strategies (ES) are effective gradient-free optimization methods that can be competitive with gradient-based approaches for policy search. ES only rely on the total episo…
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…