  We propose a model enabling decentralized multiple agents to share their
perception of environment in a fair and adaptive way. In our model, both the
current message and historical observation are taken into account, and they are
handled in the same recurrent model but in different forms. We present a
dual-level recurrent communication framework for multi-agent systems, in which
the first recurrence occurs in the communication sequence and is used to
transmit communication data among agents, while the second recurrence is based
on the time sequence and combines the historical observations for each agent.
The developed communication flow separates communication messages from memories
but allows agents to share their historical observations by the dual-level
recurrence. This design makes agents adapt to changeable communication objects,
while the communication results are fair to these agents. We provide a
sufficient discussion about our method in both partially observable and fully
observable environments. The results of several experiments suggest our method
outperforms the existing decentralized communication frameworks and the
corresponding centralized training method.
