38 citations · 96 across the 20 of their papers we have counts for
20 papers
Remotely Detectable Keyed Communication through Motion
Benjamin Chang, Michael Amir, Manon Flageat +1
Messages from electronic devices are conventionally received as text, audio, or radio signals. But robots move with rich, articulate motion in the real world, opening up the possib…
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…
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…
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…
Large Language Models as In-context AI Generators for Quality-Diversity
Bryan Lim, Manon Flageat, Antoine Cully
Quality-Diversity (QD) approaches are a promising direction to develop open-ended processes as they can discover archives of high-quality solutions across diverse niches. While alr…
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
Manon Flageat, Bryan Lim, Antoine Cully
Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment. Beyond their impact on learning, these uncertainties lead the exact…
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…
Mix-ME: Quality-Diversity for Multi-Agent Learning
Garðar Ingvarsson, Mikayel Samvelyan, Bryan Lim +3
In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient. Instead, a diverse set of high-performing solutions is often requ…
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
Felix Chalumeau, Bryan Lim, Raphael Boige +7
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization…
Benchmark tasks for Quality-Diversity applied to Uncertain domains
Manon Flageat, Luca Grillotti, Antoine Cully
While standard approaches to optimisation focus on producing a single high-performing solution, Quality-Diversity (QD) algorithms allow large diverse collections of such solutions…
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains
Luca Grillotti, Manon Flageat, Bryan Lim +1
Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the pr…
Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain Domains
Manon Flageat, Antoine Cully
Quality-Diversity optimisation (QD) has proven to yield promising results across a broad set of applications. However, QD approaches struggle in the presence of uncertainty in the…
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning
Bryan Lim, Manon Flageat, Antoine Cully
The synergies between Quality-Diversity (QD) and Deep Reinforcement Learning (RL) have led to powerful hybrid QD-RL algorithms that have shown tremendous potential, and brings the…
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Maxence Faldor, Félix Chalumeau, Manon Flageat +1
Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfu…
Enhancing MAP-Elites with Multiple Parallel Evolution Strategies
Manon Flageat, Bryan Lim, Antoine Cully
With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications,…
Efficient Exploration using Model-Based Quality-Diversity with Gradients
Bryan Lim, Manon Flageat, Antoine Cully
Exploration is a key challenge in Reinforcement Learning, especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches h…
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…
Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains
Manon Flageat, Felix Chalumeau, Antoine Cully
Quality-Diversity algorithms, among which MAP-Elites, have emerged as powerful alternatives to performance-only optimisation approaches as they enable generating collections of div…
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…