The Emergence of Complex Behavior in Large-Scale Ecological Environments

TL;DR

This paper investigates how large-scale ecological environments with many agents lead to the emergence of complex behaviors through evolution, using advanced simulation and hardware to analyze effects of scale and sensing.

Contribution

It introduces a scalable multi-agent simulation framework to study emergent behaviors in large populations without explicit rewards, highlighting the role of environmental scale and sensing.

Findings

Emergent behaviors like resource extraction, foraging, and predation observed.

Larger populations and environments increase behavior stability and diversity.

Scaling up enhances the potential for ecological-inspired machine learning methods.

Abstract

We explore how physical scale and population size shape the emergence of complex behaviors in open-ended ecological environments. In our setting, agents are unsupervised and have no explicit rewards or learning objectives but instead evolve over time according to reproduction, mutation, and selection. As they act, agents also shape their environment and the population around them in an ongoing dynamic ecology. Our goal is not to optimize a single high-performance policy, but instead to examine how behaviors emerge and evolve across large populations due to natural competition and environmental pressures. We use modern hardware along with a new multi-agent simulator to scale the environment and population to sizes much larger than previously attempted, reaching populations of over 60,000 agents, each with their own evolved neural network policy. We identify various emergent behaviors such as long-range resource extraction, vision-based foraging, and predation that arise under competitive and survival pressures. We examine how sensing modalities and environmental scale affect the emergence of these behaviors and find that some of them appear only in sufficiently large environments and populations, and that larger scales increase the stability and consistency of these emergent behaviors. While there is a rich history of research in evolutionary settings, our scaling results on modern hardware provide promising new directions to explore ecology as an instrument of machine learning in an era of increasingly abundant computational resources and efficient machine frameworks. Experimental code is available at https://github.com/jbejjani2022/ecological-emergent-behavior.