Despite the impressive progress in neural network architecture design, improving the
performance of the existing state-of-the-art models has become increasingly challenging …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …

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 …

Quality-Diversity optimization algorithms such as MAP-Elites, aim to generate collections of
both diverse and high-performing solutions to an optimization problem. MAP-Elites has …

Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a
branch of optimization that seeks to find a collection of diverse, high-performing solutions to …

Recent progress in reinforcement learning (RL) has started producing generally capable
agents that can solve a distribution of complex environments. These agents are typically …

QDax is an open-source library with a streamlined and modular API for Quality-Diversity
(QD) optimisation algorithms in Jax. The library serves as a versatile tool for optimisation …

Consider the problem of training robustly capable agents. One approach is to generate a
diverse collection of agent polices. Training can then be viewed as a quality diversity (QD) …