  Communication is an effective mechanism for coordinating the behavior of
multiple agents. In the field of multi-agent reinforcement learning, agents can
improve the overall learning performance and achieve their objectives by
communication. Moreover, agents can communicate various types of messages,
either to all agents or to specific agent groups, and through specific
channels. With the growing body of research work in MARL with communication
(Comm-MARL), there is lack of a systematic and structural approach to
distinguish and classify existing Comm-MARL systems. In this paper, we survey
recent works in the Comm-MARL field and consider various aspects of
communication that can play a role in the design and development of multi-agent
reinforcement learning systems. With these aspects in mind, we propose several
dimensions along which Comm-MARL systems can be analyzed, developed, and
compared.
