  Recent studies have shown that introducing communication between agents can
significantly improve overall performance in cooperative Multi-agent
reinforcement learning (MARL). However, existing communication schemes often
require agents to exchange an excessive number of messages at run-time under a
reliable communication channel, which hinders its practicality in many
real-world situations. In this paper, we present \textit{Temporal Message
Control} (TMC), a simple yet effective approach for achieving succinct and
robust communication in MARL. TMC applies a temporal smoothing technique to
drastically reduce the amount of information exchanged between agents.
Experiments show that TMC can significantly reduce inter-agent communication
overhead without impacting accuracy. Furthermore, TMC demonstrates much better
robustness against transmission loss than existing approaches in lossy
networking environments.
