963 citations · 1.6k across the 45 of their papers we have counts for
6 papers · 1 filter
Quality-Diversity Optimization: a novel branch of stochastic optimization
Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades +1
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highe…
Policy Manifold Search for Improving Diversity-based Neuroevolution
Nemanja Rakicevic, Antoine Cully, Petar Kormushev
Diversity-based approaches have recently gained popularity as an alternative paradigm to performance-based policy search. A popular approach from this family, Quality-Diversity (QD…
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulations
Szymon Brych, Antoine Cully
The increasing importance of robots and automation creates a demand for learnable controllers which can be obtained through various approaches such as Evolutionary Algorithms (EAs)…
Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters
Antoine Cully
Quality-Diversity (QD) optimisation is a new family of learning algorithms that aims at generating collections of diverse and high-performing solutions. Among those algorithms, the…
Fast and stable MAP-Elites in noisy domains using deep grids
Manon Flageat, Antoine Cully
Quality-Diversity optimisation algorithms enable the evolution of collections of both high-performing and diverse solutions. These collections offer the possibility to quickly adap…
Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization
Thomas Pierrot, Valentin Macé, Félix Chalumeau +6
A fascinating aspect of nature lies in its ability to produce a large and diverse collection of organisms that are all high-performing in their niche. By contrast, most AI algorith…