Towards a Better Understanding of Learning with Multiagent Teams
This addresses the problem of optimizing team learning for AI researchers, though it appears incremental as it builds on known issues in multiagent coordination.
The paper investigates why certain team structures promote effective learning in multiagent systems, finding that some structures enable role specialization for better global results, but large teams face credit assignment challenges that reduce coordination and performance.
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.