Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints
Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone swarms, operate under severe bandwidth constraints. Many communication architectures still expose a coupled bottleneck in which a shared latent representation is used for both policy execution and inter-agent communication. Consequently, reducing message size directly limits the policy's latent space, often leading to significant performance degradation. We address this with two contributions. First, we introduce $β$, a normalised per-agent bandwidth budget that unifies sparsity, rounds, and message dimension into a single comparable constra...
| Authors | Alexi Canesse, Benoît Goupil, Jesse Read et al. |
|---|---|
| Primary category | cs.MA |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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