  Multi-agent reinforcement learning (RL) has important implications for the
future of human-agent teaming. We show that improved performance with
multi-agent RL is not a guarantee of the collaborative behavior thought to be
important for solving multi-agent tasks. To address this, we present a novel
approach for quantitatively assessing collaboration in continuous spatial tasks
with multi-agent RL. Such a metric is useful for measuring collaboration
between computational agents and may serve as a training signal for
collaboration in future RL paradigms involving humans.
