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.

翻译：尽管长期以来人们认识到，由个体学习智能体组成的团队可能产生大于个体之和的效果，但近期研究表明，较大的团队并不一定比较小的团队更有效。本文研究在何种条件下，特定的团队结构能促进个体学习智能体的有效学习。我们发现，根据环境的不同，某些团队结构有助于智能体学习分化为特定角色，从而获得更优的全局结果。然而，大型团队会带来信用分配难题，削弱协调能力，导致其表现反而不如小型团队。我们通过理论分析与实验结果共同支持上述结论。