Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …

Quality diversity (QD) is a growing branch of stochastic optimization research that studies the
problem of generating an archive of solutions that maximize a given objective function but …

Quality-Diversity optimisation algorithms enable the evolution of collections of both high-
performing and diverse solutions. These collections offer the possibility to quickly adapt and …

Evolutionary optimization is difficult in domains that require heterogeneous agents to
coordinate on diverse tasks as agents often converge to a limited set of" acceptable" …

In planning problems, it is often challenging to fully model the desired specifications. In
particular, in human-robot interaction, such difficulty may arise due to human's preferences …

To coordinate in multiagent settings characterized by multiple objectives, asymmetric agents
(agents with distinct capabilities and preferences) must learn diverse behaviors to balance …

This article addresses the co-evolution of morphology and control in evolutionary robotics,
focusing on the challenge of premature convergence and limited morphological diversity …

Multiagent learning offers a rich framework to address challenging real-world problems such
as remote exploration and healthcare coordination, which require autonomous agents to …

1 Zusammenfassung Das Projekt AErOmAt hatte zum Ziel, neue Methoden zu entwickeln,
um einen erheblichen Teil aerodynamischer Simulationen bei rechenaufwändigen …