Effective diversity in population based reinforcement learning
J Parker-Holder, A Pacchiano… - Advances in …, 2020 - proceedings.neurips.cc
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 …
data they acquire in the environment. With that in mind, maintaining a population of agents is …
Differentiable quality diversity
M Fontaine, S Nikolaidis - Advances in Neural Information …, 2021 - proceedings.neurips.cc
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 …
problem of generating an archive of solutions that maximize a given objective function but …
Fast and stable MAP-Elites in noisy domains using deep grids
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 …
performing and diverse solutions. These collections offer the possibility to quickly adapt and …
Balancing teams with quality-diversity for heterogeneous multiagent coordination
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" …
coordinate on diverse tasks as agents often converge to a limited set of" acceptable" …
Multiple Plans are Better than One: Diverse Stochastic Planning
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 …
particular, in human-robot interaction, such difficulty may arise due to human's preferences …
Premature convergence in morphology and control co-evolution: a study
L Eguiarte-Morett, W Aguilar - Adaptive Behavior, 2024 - journals.sagepub.com
This article addresses the co-evolution of morphology and control in evolutionary robotics,
focusing on the challenge of premature convergence and limited morphological diversity …
focusing on the challenge of premature convergence and limited morphological diversity …
Learning to coordinate in sparse asymmetric multiagent systems
G Dixit - 2023 - ir.library.oregonstate.edu
Multiagent learning offers a rich framework to address challenging real-world problems such
as remote exploration and healthcare coordination, which require autonomous agents to …
as remote exploration and healthcare coordination, which require autonomous agents to …
[PDF][PDF] AErOmAt Abschlussbericht
A Asteroth - 2020 - researchgate.net
1 Zusammenfassung Das Projekt AErOmAt hatte zum Ziel, neue Methoden zu entwickeln,
um einen erheblichen Teil aerodynamischer Simulationen bei rechenaufwändigen …
um einen erheblichen Teil aerodynamischer Simulationen bei rechenaufwändigen …