By Topic

Swarm reinforcement learning method based on ant colony optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Iima, H. ; Dept. of Inf. Sci., Kyoto Inst. of Technol., Kyoto, Japan ; Kuroe, Y. ; Matsuda, S.

In ordinary reinforcement learning methods, a single agent learns to achieve a goal through many episodes. Since the agent essentially learns by trial and error, it takes much computation time to acquire an optimal policy especially for complicated learning problems. Meanwhile, for optimization problems, population-based methods such as particle swarm optimization have been recognized that they are able to find rapidly the global optimal solution for multi-modal functions with wide solution space. We recently proposed swarm reinforcement learning methods in which multiple agents are prepared and they learn through not only their respective experiences but also exchanging information among them. In these methods, it is important how to design a method of exchanging the information. In this paper, we propose a swarm reinforcement learning method based on ant colony optimization, which is an optimization method inspired from behavior of real ants using trail pheromones, in order to acquire the optimal policy rapidly even for complicated reinforcement learning problems. In the proposed method, the agents exchange their information through Pheromone-Q values which we define so as to make them play the same role as the trail pheromones. The proposed method is applied to shortest path problems, and its performance is demonstrated through numerical experiments.

Published in:

Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on

Date of Conference:

10-13 Oct. 2010