Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints

Researchers introduced SLIM, a multi-agent reinforcement learning communication architecture designed for tight bandwidth limits.

It uses a normalized bandwidth metric and a minimal design that decouples messaging from policy representation.

The approach achieved state-of-the-art results on partially observable benchmarks and stayed robust as communication budgets shrank.

The work suggests agents can coordinate efficiently without sacrificing policy performance, which matters for real-world systems like drone swarms.