  In nature, flocking or swarm behavior is observed in many species as it has
beneficial properties like reducing the probability of being caught by a
predator. In this paper, we propose SELFish (Swarm Emergent Learning Fish), an
approach with multiple autonomous agents which can freely move in a continuous
space with the objective to avoid being caught by a present predator. The
predator has the property that it might get distracted by multiple possible
preys in its vicinity. We show that this property in interaction with
self-interested agents which are trained with reinforcement learning to solely
survive as long as possible leads to flocking behavior similar to Boids, a
common simulation for flocking behavior. Furthermore we present interesting
insights in the swarming behavior and in the process of agents being caught in
our modeled environment.
