Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem

TL;DR

This paper provides a theoretical analysis of Quality Diversity algorithms applied to a classical path planning problem, demonstrating their efficiency in computing shortest paths and exploring parent selection techniques for improved performance.

Contribution

It offers the first theoretical insights into QD algorithms' behavior on path planning, showing their ability to compute all-pairs shortest paths efficiently and analyzing parent selection methods.

Findings

Map-Elites QD algorithms efficiently compute shortest paths in parallel.

Parent selection techniques significantly speed up the QD process.

Theoretical understanding of QD algorithms' behavior in classical planning.

Abstract

Quality diversity (QD) algorithms have shown to provide sets of high quality solutions for challenging problems in robotics, games, and combinatorial optimisation. So far, theoretical foundational explaining their good behaviour in practice lack far behind their practical success. We contribute to the theoretical understanding of these algorithms and study the behaviour of QD algorithms for a classical planning problem seeking several solutions. We study the all-pairs-shortest-paths (APSP) problem which gives a natural formulation of the behavioural space based on all pairs of nodes of the given input graph that can be used by Map-Elites QD algorithms. Our results show that Map-Elites QD algorithms are able to compute a shortest path for each pair of nodes efficiently in parallel. Furthermore, we examine parent selection techniques for crossover that exhibit significant speed ups compared to the standard QD approach.