Quantifying the Effects of Increasing User Choice in MAP-Elites Applied to a Workforce Scheduling and Routing Problem

Urquhart, Neil; Hart, Emma; Hutcheson, William

doi:10.1007/978-3-030-16692-2_4

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…QD aims at generating a collection of diverse solutions that are all as high-performing as possible and as different as possible from each other. This approach has been applied to many domains, such as in robotics to learn a diverse set of high-performing controllers [7,40], in video-games to generate a variety of dungeons [2] or card decks [14], or in workforce scheduling and routing tasks [39].…”

Section: Related Work 21 Quality Diversity Algorithmsmentioning

confidence: 99%

Multi-emitter MAP-elites

Cully

2021

Proceedings of the Genetic and Evolutionary Computation Conference

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Quality-Diversity (QD) optimisation is a new family of learning algorithms that aims at generating collections of diverse and highperforming solutions. Among those algorithms, the recently introduced Covariance Matrix Adaptation MAP-Elites (CMA-ME) algorithm proposes the concept of emitters, which uses a predefined heuristic to drive the algorithm's exploration. This algorithm was shown to outperform MAP-Elites, a popular QD algorithm that has demonstrated promising results in numerous applications. In this paper, we introduce Multi-Emitter MAP-Elites (ME-MAP-Elites), an algorithm that directly extends CMA-ME and improves its quality, diversity and data efficiency. It leverages the diversity of a heterogeneous set of emitters, in which each emitter type improves the optimisation process in different ways. A bandit algorithm dynamically finds the best selection of emitters depending on the current situation. We evaluate the performance of ME-MAP-Elites on six tasks, ranging from standard optimisation problems (in 100 dimensions) to complex locomotion tasks in robotics. Our comparisons against CMA-ME and MAP-Elites show that ME-MAP-Elites is faster at providing collections of solutions that are significantly more diverse and higher performing. Moreover, in cases where no fruitful synergy can be found between the different emitters, ME-MAP-Elites is equivalent to the best of the compared algorithms. CCS CONCEPTS• Computing methodologies → Evolutionary robotics; • Theory of computation → Stochastic control and optimization.

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Section: Related Work 21 Quality Diversity Algorithmsmentioning

confidence: 99%

Multi-emitter MAP-elites

Cully

2021

Proceedings of the Genetic and Evolutionary Computation Conference

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“…QD aims at generating a collection of diverse solutions that are all as highperforming as possible and as different as possible from each other. This approach has been applied to many domains, such as in robotics to learn a diverse set of high-performing controllers (Cully, 2019;, in video-games to generate a variety of dungeons (Alvarez et al, 2019) or card decks (Fontaine et al, 2019), or in workforce scheduling and routing problems (Urquhart et al, 2019).…”

Section: Related Work Map-elites a Quality Diversity Algorithmmentioning

confidence: 99%

Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters

Cully

2020

Preprint

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Quality-Diversity (QD) optimisation is a new family of learning algorithms that aims at generating collections of diverse and high-performing solutions. Among those algorithms, MAP-Elites is a simple yet powerful approach that has shown promising results in numerous applications. In this paper, we introduce a novel algorithm named Multi-Emitter MAP-Elites (ME-MAP-Elites) that improves the quality, diversity and convergence speed of MAP-Elites. It is based on the recently introduced concept of emitters, which are used to drive the algorithm's exploration according to predefined heuristics. ME-MAP-Elites leverages the diversity of a heterogeneous set of emitters, in which each emitter type is designed to improve differently the optimisation process. Moreover, a bandit algorithm is used to dynamically find the best emitter set depending on the current situation. We evaluate the performance of ME-MAP-Elites on six tasks, ranging from standard optimisation problems (in 100 dimensions) to complex locomotion tasks in robotics. Our comparisons against MAP-Elites and existing approaches using emitters show that ME-MAP-Elites is faster at providing collections of solutions that are significantly more diverse and higher performing. Moreover, in the rare cases where no fruitful synergy can be found between the different emitters, ME-MAP-Elites is equivalent to the best of the compared algorithms.

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