Succinct and Robust Multi-Agent Communication With Temporal Message Control
By Sai Qian Zhang, Jieyu Lin, Qi Zhang. 2020
84 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
Citations in the corpus
- Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing (2021, background): “, 2020), agent modelling (Albrecht & Stone, 2018), and communication between agents (Rangwala & Williams, 2020; Zhang et al., 2020) to address the non-stationarity and multi-agent credit assignment problems (Papoudakis et al.”
- A survey of multi-agent deep reinforcement learning with communication (2022, unknown): “Comm-MADRL approaches like VBC [31], NDQ [75], TMC [32], and MAIC [33] utilize handcrafted thresholds or pruning rates to limit communication opportunities.”