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

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Heterogeneous Information-Bottleneck Coordination Graphs (HIBCG) is a novel method for cooperative multi-agent reinforcement learning (MARL) that addresses limitations in existing sparse coordination graph learners. Unlike prior methods that use heuristic criteria for edge existence and homogeneous communication capacities, HIBCG provides a theoretically grounded mechanism for both. It learns a group-aware sparse graph where edge existence and message capacity are justified by the graph information bottleneck (GIB). HIBCG first constructs a group-aligned block-diagonal prior for edge retention, enabling differential control over intra-group and inter-group connections, and then compresses per-agent feature bandwidth to retain only task-relevant content. The method demonstrates state-of-the-art coordination performance on SMACv1, SMACv2, and MAgent Battle benchmarks, particularly excelling in heterogeneous multi-role tasks and large-scale scenarios with up to 100 agents, where it shows significant gains and improved convergence compared to existing approaches.

Key takeaway

For research scientists developing multi-agent reinforcement learning systems, HIBCG offers a principled approach to learning communication topologies. You should consider implementing its group-aligned prior and dual AIB/XIB compression paths to achieve superior coordination, especially in heterogeneous, multi-role, or large-scale environments. This method provides theoretical guarantees for efficient information allocation, leading to more robust and scalable MARL solutions.

Key insights

HIBCG uses a GIB-based, group-aware approach to learn sparse, heterogeneous coordination graphs for MARL.

Principles

• Decompose information flow into topology and content paths.
• Utilize group-aligned priors for tighter variational bounds.
• Allocate compression based on water-filling principle.

Method

HIBCG employs a three-stage architecture: GACG for initial graph and group partition, per-layer Gaussian structural encoders for topology refinement via AIB, and per-agent XIB encoders for message compression.

In practice

• Apply group-aware priors to improve MARL coordination.
• Use asymmetric pruning for inter-group vs. intra-group edges.
• Integrate AIB and XIB for complementary compression benefits.

Topics

• Multi-Agent Reinforcement Learning
• Coordination Graphs
• Information Bottleneck
• Graph Neural Networks
• Heterogeneous Graph Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.