Contributed talk in Swarm Behaviour 1, Aug. 1, 2019, noon in room NUBS 2.03

Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning

Carsten Hahn, Thomy Phan, Thomas Gabor, Lenz Belzner, Claudia Linnhoff-Popien

watch Publication 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 consider the scenario of 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 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.