Towards a Better Understanding of Learning with Multiagent Teams

TL;DR

This paper investigates how team size and structure influence learning effectiveness in multiagent systems, highlighting the trade-offs between specialization benefits and coordination challenges in different environments.

Contribution

It provides a theoretical and empirical analysis of how team structures affect learning outcomes, emphasizing the role of environment and team size.

Findings

Smaller teams often outperform larger ones due to better coordination.

Certain team structures facilitate role specialization, improving global results.

Large teams face credit assignment issues that hinder performance.

Abstract

While it has long been recognized that a team of individual learning agents can be greater than the sum of its parts, recent work has shown that larger teams are not necessarily more effective than smaller ones. In this paper, we study why and under which conditions certain team structures promote effective learning for a population of individual learning agents. We show that, depending on the environment, some team structures help agents learn to specialize into specific roles, resulting in more favorable global results. However, large teams create credit assignment challenges that reduce coordination, leading to large teams performing poorly compared to smaller ones. We support our conclusions with both theoretical analysis and empirical results.