Traditional optimization algorithms search for a single global optimum that maximizes (or
minimizes) the objective function. Multimodal optimization algorithms search for the highest …

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 …

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) …

Comparing the functional behavior of neural network models, whether it is a single network
over time or two (or more networks) during or post-training, is an essential step in …

We study the problem of efficiently generating high-quality and diverse content in games.
Previous work on automated deckbuilding in Hearthstone shows that the quality diversity …

Quality-Diversity algorithms, among which are the Multi-dimensional Archive of Phenotypic
Elites (MAP-Elites), have emerged as powerful alternatives to performance-only optimisation …

Neuroevolution is an alternative to gradient-based optimisation that has the potential to
avoid local minima and allows parallelisation. The main limiting factor is that usually it does …

Several works have demonstrated the use of variational autoencoders (VAEs) for generating
levels in the style of existing games and blending levels across different games. Further …

Abstract Recent progress in Quality Diversity Reinforcement Learning (QD-RL) has enabled
learning a collection of behaviorally diverse, high performing policies. However, these …