The impact of behavioral diversity in multi-agent reinforcement learning

TL;DR

This paper investigates how behavioral diversity among agents in multi-agent reinforcement learning enhances collective performance, revealing that controlling diversity leads to better cooperation, skill retention, and problem-solving in complex tasks.

Contribution

It introduces diversity measurement and control methods to study behavioral heterogeneity, demonstrating its benefits over homogeneous strategies in multi-agent reinforcement learning.

Findings

Behavioral heterogeneity leads to emergent unbiased roles that improve team outcomes.

Diversity synergizes with morphological differences to enhance cooperation.

Heterogeneous teams are more effective in sparse reward environments and retain latent skills.

Abstract

Many of the world's most pressing issues, such as climate change and global peace, require complex collective problem-solving skills. Recent studies indicate that diversity in individuals' behaviors is key to developing such skills and increasing collective performance. Yet behavioral diversity in collective artificial learning is understudied, with today's machine learning paradigms commonly favoring homogeneous agent strategies over heterogeneous ones, mainly due to computational considerations. In this work, we employ diversity measurement and control paradigms to study the impact of behavioral heterogeneity in several facets of multi-agent reinforcement learning. Through experiments in team play and other cooperative tasks, we show the emergence of unbiased behavioral roles that improve team outcomes; how behavioral diversity synergizes with morphological diversity; how diverse agents are more effective at finding cooperative solutions in sparse reward settings; and how behaviorally heterogeneous teams learn and retain latent skills to overcome repeated disruptions. Overall, our results indicate that, by controlling diversity, we can obtain non-trivial benefits over homogeneous training paradigms, demonstrating that diversity is a fundamental component of collective artificial learning, an insight thus far overlooked.