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Found 11 papers, 5 papers with code
Quality with Just Enough Diversity in Evolutionary Policy Search
Evolution Strategies (ES) are effective gradient-free optimization methods that can be competitive with gradient-based approaches for policy search.
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations.
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.
Discovering Unsupervised Behaviours from Full-State Trajectories
We evaluate this approach on a simulated robotic environment, where the robot has to autonomously discover its abilities from its full-state trajectories.
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.
Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms
Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks.
Accelerated Quality-Diversity through Massive Parallelism
With recent advances in simulators that run on accelerators, thousands of evaluations can now be performed in parallel on single GPU/TPU.
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires
In this paper, we propose Dynamics-Aware Quality-Diversity (DA-QD), a framework to improve the sample efficiency of QD algorithms through the use of dynamics models.
Unsupervised Behaviour Discovery with Quality-Diversity Optimisation
In robotics, such algorithms can be used for generating a collection of controllers covering most of the possible behaviours of a robot.
