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

Hahn, Carsten; Phan, Thomy; Gabor, Thomas; Belzner, Lenz and Linnhoff-Popien, Claudia (2019): Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning. In: Alife 2019: the 2019 Conference on Artificial Life: pp. 598-605

Abstract

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 solely to survive as long as possible leads to flocking behavior similar to Boids, a common simulation for flocking behavior. Furthermore we present interesting insights into the swarming behavior and into the process of agents being caught in our modeled environment.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Language: English