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JACIII Vol.11 No.2 pp. 155-161
doi: 10.20965/jaciii.2007.p0155
(2007)

Paper:

Reinforcement Learning Scheme for Flocking Behavior Emergence

Koichiro Morihiro*, **, Teijiro Isokawa**, Haruhiko Nishimura***, Masahito Tomimasu**, Naotake Kamiura**, and Nobuyuki Matsui**

*Hyogo University of Teacher Education, 942-1 Shimokume, Kato-shi, Hyogo 673-1494, Japan

**Division of Computer Engineering, Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2201, Japan

***Graduate School of Applied Informatics, University of Hyogo, 1-3-3 Chuo-ku, Kobe, Hyogo 650-0044, Japan

Received:
October 24, 2005
Accepted:
December 22, 2006
Published:
February 20, 2007
Keywords:
collective behavior, flocking, perceptual internal space, reinforcement learning, Q-learning
Abstract

Collective behavior such as bird flocking, land animal herding, and fish schooling is well known in nature. Many observations have shown that there are no leaders to control the behavior of a group. Several models have been proposed for describing the grouping behavior, which we regard as a distinctive example of aggregate motions. In these models, a fixed rule is provided for each of the individuals a priori for their interactions in a reductive and rigid manner. In contrast, we propose a new framework for the self-organized grouping of agents by reinforcement learning. It is important to introduce a learning scheme for causing collective behavior in artificial autonomous distributed systems. The behavior of agents is demonstrated and evaluated through computer simulations and it is shown that their grouping behavior emerges as a result of learning.

Cite this article as:
Koichiro Morihiro, Teijiro Isokawa, Haruhiko Nishimura, Masahito Tomimasu, Naotake Kamiura, and Nobuyuki Matsui, “Reinforcement Learning Scheme for Flocking Behavior Emergence,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.2, pp. 155-161, 2007.
Data files:
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Last updated on Jun. 15, 2021