When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL
Leveraging belief distribution divergence to optimize communication efficiency in multi-agent reinforcement learning.
Effective communication in multi-agent reinforcement learning requires agents to decide not only \textit{what} to communicate, but when? Existing approaches either communicate at every timestep or learn a binary gate through REINFORCE policy gradients \cite{singh2019}, a high-var...
ArXiv CS.AI
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This research introduces a more stable, interpretable mechanism for multi-agent communication by utilizing KL divergence to measure belief agreement between agents. By replacing noisy reinforcement learning signals with a principled threshold, it achieves superior coordination and consistency in complex multi-agent environments.
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