Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains
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| Názov: | Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains |
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| Autori: | Flageat, Manon, Huber, Johann, Helenon, François, Doncieux, Stephane, Cully, Antoine |
| Prispievatelia: | Huber, Johann |
| Zdroj: | Proceedings of the Genetic and Evolutionary Computation Conference. :140-148 |
| Publication Status: | Preprint |
| Informácie o vydavateľovi: | ACM, 2025. |
| Rok vydania: | 2025 |
| Predmety: | [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE) |
| Popis: | Quality-Diversity (QD) has demonstrated potential in discovering collections of diverse solutions to optimisation problems. Originally designed for deterministic environments, QD has been extended to noisy, stochastic, or uncertain domains through various Uncertain-QD (UQD) methods. However, the large number of UQD methods, each with unique constraints, makes selecting the most suitable one challenging. To remedy this situation, we present two contributions: first, the Extract-QD Framework (EQD Framework), and second, Extract-ME (EME), a new method derived from it. The EQD Framework unifies existing approaches within a modular view, and facilitates developing novel methods by interchanging modules. We use it to derive EME, a novel method that consistently outperforms or matches the best existing methods on standard benchmarks, while previous methods show varying performance. In a second experiment, we show how our EQD Framework can be used to augment existing QD algorithms and in particular the well-established Policy-Gradient-Assisted-MAP-Elites method, and demonstrate improved performance in uncertain domains at no additional evaluation cost. For any new uncertain task, our contributions now provide EME as a reliable "first guess" method, and the EQD Framework as a tool for developing task-specific approaches. Together, these contributions aim to lower the cost of adopting UQD insights in QD applications. |
| Druh dokumentu: | Article Conference object |
| DOI: | 10.1145/3712256.3726404 |
| DOI: | 10.48550/arxiv.2502.06585 |
| Prístupová URL adresa: | http://arxiv.org/abs/2502.06585 https://hal.science/hal-05313280v1 https://doi.org/10.1145/3712256.3726404 |
| Rights: | CC BY |
| Prístupové číslo: | edsair.doi.dedup.....73af770b5b8d1a4b87c32a2645af99fb |
| Databáza: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | Quality-Diversity (QD) has demonstrated potential in discovering collections of diverse solutions to optimisation problems. Originally designed for deterministic environments, QD has been extended to noisy, stochastic, or uncertain domains through various Uncertain-QD (UQD) methods. However, the large number of UQD methods, each with unique constraints, makes selecting the most suitable one challenging. To remedy this situation, we present two contributions: first, the Extract-QD Framework (EQD Framework), and second, Extract-ME (EME), a new method derived from it. The EQD Framework unifies existing approaches within a modular view, and facilitates developing novel methods by interchanging modules. We use it to derive EME, a novel method that consistently outperforms or matches the best existing methods on standard benchmarks, while previous methods show varying performance. In a second experiment, we show how our EQD Framework can be used to augment existing QD algorithms and in particular the well-established Policy-Gradient-Assisted-MAP-Elites method, and demonstrate improved performance in uncertain domains at no additional evaluation cost. For any new uncertain task, our contributions now provide EME as a reliable "first guess" method, and the EQD Framework as a tool for developing task-specific approaches. Together, these contributions aim to lower the cost of adopting UQD insights in QD applications. |
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| DOI: | 10.1145/3712256.3726404 |