Reference
J. van Ast, R. Babuška, and B. De Schutter, "Convergence analysis of ant
colony learning,"
Proceedings of the 18th IFAC World
Congress, Milan, Italy, pp. 14693-14698, Aug.-Sept. 2011.
Abstract
In this paper, we study the convergence of the pheromone levels of Ant Colony
Learning (ACL) in the setting of discrete state spaces and noiseless state
transitions. ACL is a multi-agent approach for learning control policies that
combines some of the principles found in ant colony optimization and
reinforcement learning. Convergence of the pheromone levels in expected value
is a necessary requirement for the convergence of the learning process to
optimal control policies. In this paper, we derive upper and lower bounds for
the pheromone levels and relate those to the learning parameters and the number
of ants used in the algorithm. We also derive upper and lower bounds on the
expected value of the pheromone levels.
Publisher page
Downloads
BibTeX
@inproceedings{vanBab:11-012,
author = {van Ast, Jelmer and Babu{\v{s}}ka, Robert and De Schutter,
Bart},
title = {Convergence Analysis of Ant Colony Learning},
booktitle = {Proceedings of the 18th IFAC World Congress},
address = {Milan, Italy},
pages = {14693--14698},
month = aug # {--} # sep,
year = {2011}
}