When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL
Researchers propose a new approach to communication in multi-agent reinforcement learning. Instead of communicating at every timestep or using a binary gate, agents only communicate when the difference between their learned belief distributions exceeds a certain threshold. This method is evaluated on two environments and compared to existing approaches, showing improved performance in some cases.
Researchers propose a new approach to communication in multi-agent reinforcement learning. Instead of communicating at every timestep or using a binary gate, agents only communicate when the difference between their learned belief distributions exceeds a certain threshold. This method is evaluated on two environments and compared to existing approaches, showing improved performance in some cases.
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Why it matters: This matters because it provides a principled way for agents to decide when to communicate, which can lead to more efficient and effective coordination in multi-agent systems.
Source: https://arxiv.org/abs/2608.14559
This article was originally published at: https://arxiv.org/abs/2608.14559