Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning
The paper introduces Heterogeneous Information-Bottleneck Coordination Graphs (HIBCG) for multi-agent reinforcement learning. It addresses the limitations of existing sparse-graph learners by providing a theoretically grounded mechanism for edge existence and communication capacity. The proposed method enhances the learning of agent relationships and optimizes message content for task relevance.
- ▪HIBCG constructs a group-aware sparse graph with justified edge existence and message capacity.
- ▪The graph information bottleneck serves as the underlying tool for the proposed method.
- ▪The approach allows for differential edge control and follows a water-filling principle for capacity allocation.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17393 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.17393 (cs) [Submitted on 17 May 2026] Title:Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning Authors:Wei Duan, Junyu Xuan, En Yu, Xiaoyu Yang, Jie Lu View a PDF of the paper titled Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning, by Wei Duan and 4 other authors View PDF HTML (experimental) Abstract: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.
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