  The role concept provides a useful tool to design and understand complex
multi-agent systems, which allows agents with a similar role to share similar
behaviors. However, existing role-based methods use prior domain knowledge and
predefine role structures and behaviors. In contrast, multi-agent reinforcement
learning (MARL) provides flexibility and adaptability, but less efficiency in
complex tasks. In this paper, we synergize these two paradigms and propose a
role-oriented MARL framework (ROMA). In this framework, roles are emergent, and
agents with similar roles tend to share their learning and to be specialized on
certain sub-tasks. To this end, we construct a stochastic role embedding space
by introducing two novel regularizers and conditioning individual policies on
roles. Experiments show that our method can learn specialized, dynamic, and
identifiable roles, which help our method push forward the state of the art on
the StarCraft II micromanagement benchmark. Demonstrative videos are available
at https://sites.google.com/view/romarl/.
