Succinct and Robust Multi-Agent Communication With Temporal Message Control
This addresses communication inefficiency and unreliability for multi-agent systems in real-world applications, representing an incremental improvement over existing methods.
The paper tackles the problem of excessive and unreliable communication in cooperative multi-agent reinforcement learning by introducing Temporal Message Control (TMC), which reduces communication overhead by 30-50% without accuracy loss and improves robustness against transmission loss.
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.