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

被引：0

作者：

Hahn, Carsten ^{[1
]}

Phan, Thomy ^{[1
]}

Gabor, Thomas ^{[1
]}

Belzner, Lenz ^{[2
]}

Linnhoff-Popien, Claudia ^{[1
]}

机构：

[1] Ludwig Maximilians Univ Munchen, Mobile & Distributed Syst Grp, Munich, Germany

[2] MaibornWolff, Munich, Germany

来源：

ALIFE 2019: THE 2019 CONFERENCE ON ARTIFICIAL LIFE | 2019年

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：598 / 605

页数：8

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