Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning

In three linesHIBCG introduces a theoretically grounded approach to learn sparse coordination graphs in multi-agent RL. Using graph information bottleneck, it determines edge existence and message capacity with formal guarantees on learned topology and differential capacity allocation across agent groups.

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