5 papers
Events as Triggers for Behavioral Diversity in Multi-Agent Reinforcement Learning
Hannes Büchi, Manon Flageat, Eduardo Sebastián +1
Effective multi-agent cooperation requires agents to adopt diverse behaviors as task conditions evolve-and to do so at the right moment. Yet, current Multi-Agent Reinforcement Lear…
Remotely Detectable Robot Policy Watermarking
Michael Amir, Manon Flageat, Amanda Prorok
The success of machine learning for real-world robotic systems has created a new form of intellectual property: the trained policy. This raises a critical need for novel methods th…
Exploring the Performance-Reproducibility Trade-off in Quality-Diversity
Manon Flageat, Hannah Janmohamed, Bryan Lim +1
Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions rema…
Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains
Manon Flageat, Johann Huber, François Helenon +2
Quality-Diversity (QD) has demonstrated potential in discovering collections of diverse solutions to optimisation problems. Originally designed for deterministic environments, QD h…
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
Maxence Faldor, Félix Chalumeau, Manon Flageat +1
A hallmark of intelligence is the ability to exhibit a wide range of effective behaviors. Inspired by this principle, Quality-Diversity algorithms, such as MAP-Elites, are evolutio…