Search Results for author:
Found 15 papers, 7 papers with code
Large Language Models as In-context AI Generators for Quality-Diversity
We introduce In-context QD, a framework of techniques that aim to elicit the in-context capabilities of pre-trained Large Language Models (LLMs) to generate interesting solutions using the QD archive as context.
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
We highlight that existing procedures that only use the expected return are limited on two fronts: first an infinite number of return distributions with a wide range of performance-reproducibility trade-offs can have the same expected return, limiting its effectiveness when used for comparing policies; second, the expected return metric does not leave any room for practitioners to choose the best trade-off value for considered applications.
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
A fundamental trait of intelligence involves finding novel and creative solutions to address a given challenge or to adapt to unforeseen situations.
Mix-ME: Quality-Diversity for Multi-Agent Learning
In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient.
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax.
Benchmark tasks for Quality-Diversity applied to Uncertain domains
In this paper, we propose a first set of benchmark tasks to analyse and estimate the performance of UQD algorithms.
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains
Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space.
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning
However, we also find that not all insights from Deep RL can be effectively translated to QD-RL.
Enhancing MAP-Elites with Multiple Parallel Evolution Strategies
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, have seen their potential multiplied.
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfully applied to a variety of domains and particularly in evolutionary robotics.
Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain Domains
Second, we propose a new methodology to evaluate Uncertain QD approaches, relying on a new per-generation sampling budget and a set of existing and new metrics specifically designed for Uncertain QD.
Efficient Exploration using Model-Based Quality-Diversity with Gradients
Methods such as Quality-Diversity deals with this by encouraging novel solutions and producing a diversity of behaviours.
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control.
Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains
Secondly, we show that in addition to outperforming all the considered baselines, the collections of solutions generated by PGA-MAP-Elites are highly reproducible in uncertain environments, approaching the reproducibility of solutions found by Quality-Diversity approaches built specifically for uncertain applications.
Fast and stable MAP-Elites in noisy domains using deep grids
It therefore finds many applications in real-world domain problems such as robotic control.
