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arXiv AI By Teoman Kaman

When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL

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The paper proposes a principled communication strategy for multi‑agent reinforcement learning that gates messages based on the KL divergence between agents’ belief distributions over a latent world state. Each agent maintains a softmax belief derived from its LSTM hidden state and only communicates when disagreement exceeds a fixed threshold. Experiments on Predator‑Prey and MPE simple_spread show that this KL‑belief gating can match or surpass existing methods, improving performance and reducing variance in certain settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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