Coordination graphs are a central abstraction in cooperative multi-agent reinforcement learning (MARL), yet existing sparse-graph learners lack a theoretically grounded mechanism to decide which edges should exist and how much information each edge should carry. Current methods rely on heuristic criteria that offer no formal guarantee on the learned topology, and no principled way to allocate different communication capacities to structurally different agent relationships. To address this, we propose Heterogeneous Information-Bottleneck Coordination Graphs (HIBCG), which learns a group-aware sparse graph in which both edge existence and message capacity are theoretically justified. With the graph information bottleneck (GIB) serving as the underlying tool, HIBCG first constructs a group-aligned block-diagonal prior that provides a closed-form criterion for edge retention -- determining which edges should exist and at what density per group block -- and then controls per-agent feature bandwidth on the resulting topology, compressing messages to retain only task-relevant content. We prove that the group-aligned prior strictly tightens the variational bound on topology learning, that the objective decomposes per group block, enabling differential edge control, and that capacity allocation follows a water-filling principle.

翻译：协调图是合作多智能体强化学习（MARL）中的核心抽象概念，然而现有的稀疏图学习方法缺乏理论上严谨的机制来决定哪些边应当存在以及每条边应携带多少信息量。当前方法依赖于启发式准则，既无法对所学拓扑结构提供形式化保证，也无法以原则性方式为结构上不同的智能体关系分配不同的通信容量。为解决这一问题，我们提出了异构信息瓶颈协调图（HIBCG），该方法学习一种群体感知的稀疏图，其中边的存在性和消息容量均具有理论依据。以图信息瓶颈（GIB）为基础工具，HIBCG首先构建一个群体对齐的分块对角先验，该先验为边的保留提供了闭式判别准则——决定哪些边应当存在以及每个群体块的边密度——然后对所获拓扑结构上的每个智能体特征带宽进行控制，将消息压缩至仅保留任务相关内容。我们证明了群体对齐先验严格紧化了拓扑学习的变分界，目标函数可按群体块分解从而实现差异化边控制，并且容量分配遵循注水原理。