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Succinct and Robust Multi-Agent Communication With Temporal Message Control

Sai Qian Zhang · Qi Zhang · Jieyu Lin

Poster Session 2 #578

Keywords: [ Algorithms ] [ Structured Prediction ] [ Algorithms -> Large Margin Methods; Deep Learning ] [ Efficient Training Methods ]


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.

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