  Cooperative multi-agent reinforcement learning (MARL) is making rapid
progress for solving tasks in a grid world and real-world scenarios, in which
agents are given different attributes and goals, resulting in different
behavior through the whole multi-agent task. In this study, we quantify the
agent's behavior difference and build its relationship with the policy
performance via {\bf Role Diversity}, a metric to measure the characteristics
of MARL tasks. We define role diversity from three perspectives: action-based,
trajectory-based, and contribution-based to fully measure a multi-agent task.
Through theoretical analysis, we find that the error bound in MARL can be
decomposed into three parts that have a strong relation to the role diversity.
The decomposed factors can significantly impact policy optimization on three
popular directions including parameter sharing, communication mechanism, and
credit assignment. The main experimental platforms are based on {\bf Multiagent
Particle Environment (MPE)} and {\bf The StarCraft Multi-Agent Challenge
(SMAC). Extensive experiments} clearly show that role diversity can serve as a
robust measurement for the characteristics of a multi-agent cooperation task
and help diagnose whether the policy fits the current multi-agent system for a
better policy performance.
