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Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning

by Erik Dries
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Current price ₹1,344.00
Original price ₹1,512.00
Original price ₹1,512.00
Original price ₹1,512.00
(-11%)
₹1,344.00
Current price ₹1,344.00

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Book cover type: Paperback
  • ISBN13: 9781025136325
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Hutson Street Press
  • Publisher Imprint: Hutson Street Press
  • Publication Date:
  • Pages: 92
  • Original Price: GBP 11.95
  • Language: English
  • Edition: N/A
  • Item Weight: 141 grams
  • BISAC Subject(s): Artificial Intelligence / General

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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This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.

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