Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning

Dries, Erik

ISBN 10: 1025131622 / ISBN 13: 9781025131627

Published by Hutson Street Press, 2025

About this Item

Seller Inventory # 50615190-n

Bibliographic Details

• Title: Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning
• Publisher: Hutson Street Press
• Publication Date: 2025
• Language: English
• ISBN 10: 1025131622
• ISBN 13: 9781025131627
• Binding: Hardcover
• Condition: New

About this title

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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