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

arXiv: https://arxiv.org/abs/1905.04077 · HTML: https://mlsanity.com/paper/1905.04077/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms](https://mlsanity.com/paper/2401.07056/) (2024)
- [Learning from Shader Program Traces](https://mlsanity.com/paper/2102.04533/) (2021)
- [Unveiling Complex Collective Behaviors from Simple Rewards](https://mlsanity.com/paper/2607.12861/) (2026)
- [Boids of a Feather Flock Together - Evolving Prey Behaviours Under Different Predator Attack Strategies](https://mlsanity.com/paper/2609.37885/) (2026)

## Abstract (arXiv)

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.