Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning - Couverture rigide

Dries, Erik

Couverture rigide

ISBN 10 : 1025131622 ISBN 13 : 9781025131627

Editeur : Hutson Street Press, 2025

Synopsis

This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich's MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning and SARSA, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions not significantly different from MAXQ-Q in 88% of the time. This research then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP. A data clustering algorithm creates these subtasks, with an ACO algorithm to solve the individual and complete problems. This research tests two clustering algorithms, k-means and G-means.

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Détails de l'édition

• Éditeur: Hutson Street Press
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 1025131622
• ISBN 13: 9781025131627
• Reliure: Relié
• Nombre de pages: 92
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible