Emergence of Flocking Behavior Based on Reinforcement Learning

Abstract

Grouping motion, such as bird flocking, land animal herding, and fish schooling, is well-known in nature. Many observations have shown that there are no leading agents to control the behavior of the group. Several models have been proposed for describing the flocking behavior, which we regard as a distinctive example of the aggregate motions. In these models, some fixed rule is given to each of the individuals a priori for their interactions in reductive and rigid manner. Instead of this, we have proposed a new framework for self-organized flocking of agents by reinforcement learning. It will become important to introduce a learning scheme for making collective behavior in artificial autonomous distributed systems. In this paper, anti-predator behaviors of agents are examined by our scheme through computer simulations. We demonstrate the feature of behavior under two learning modes against agents of the same kind and predators.