做多智能体系统或机器人集群的团队，终于有了一个能直接应对带宽瓶颈的架构——SLIM 解耦通信与策略，带宽砍半性能也不崩，值得在无人机或边缘设备场景里试试。
多智能体强化学习（MARL）中，通信是实现协作的关键，但许多实际应用（如无人机群搜索救援）面临严重的带宽限制。现有通信架构通常将共享潜在表示同时用于策略执行和智能体间通信，导致减少消息大小会直接限制策略的潜在空间，造成性能显著下降。本文提出两个贡献：一是引入归一化的每智能体带宽预算 β，统一了稀疏性、轮次和消息维度；二是提出 SLIM 架构，将通信路径与策略的潜在表示解耦，从而隔离带宽对策略容量的影响。实验表明，SLIM 在多个部分可观测的 MARL 基准上达到最先进性能，并在带宽减少时仅出现轻微性能下降，展现出良好的可扩展性和鲁棒性。
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 constraint. Second, we provide SLIM, a minimal architecture that decouples the communication pathway from the policy's latent representation, allowing us to isolate the effect of bandwidth from the effect of policy capacity while benefiting from in-step communication. We evaluate our method on several partially-observable MARL benchmarks, where communication is essential. Our approach achieves state-of-the-art performance and exhibits scalability and robustness under limited communication, with only marginal degradation as bandwidth is reduced.