  For problems requiring cooperation, many multiagent systems implement
solutions among either individual agents or across an entire population towards
a common goal. Multiagent teams are primarily studied when in conflict;
however, organizational psychology (OP) highlights the benefits of teams among
human populations for learning how to coordinate and cooperate. In this paper,
we propose a new model of multiagent teams for reinforcement learning (RL)
agents inspired by OP and early work on teams in artificial intelligence. We
validate our model using complex social dilemmas that are popular in recent
multiagent RL and find that agents divided into teams develop cooperative
pro-social policies despite incentives to not cooperate. Furthermore, agents
are better able to coordinate and learn emergent roles within their teams and
achieve higher rewards compared to when the interests of all agents are
aligned.
