Learning group collaboration for efficient multi-agent reinforcement learning

Qide Wang

doi:10.1016/j.knosys.2026.116612

Learning group collaboration for efficient multi-agent reinforcement learning

2026-07-01

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DOI：10.1016/j.knosys.2026.116612

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Sequence modeling provides a self-attention mechanism-guided full-scale communication for multi-agent decision-making, showing potential in cooperative tasks. However, message sharing among all agents involves redundant observations and computational overhead. When dealing with complex multi-agent systems, it becomes difficult to distinguish valuable information for collaboration under limited exploration, leading to poor action prediction. In this paper, we propose a novel framework named group-based Multi-Agent Transformer (g-MAT). Guided by the role-discovery mechanisms, it constructs a compressed communication topology and achieves an efficient sequential policy. Specifically, the agents are divided into several role-labeled groups according to the behavioral effects. Then, the hybrid attention layer is designed to model the high-level interrelationships among agents based on the grouping scheme, where the local information from teammates and global information from other groups helps to promote collaboration. Extensive experiments on the challenging benchmarks demonstrate the superiority of g-MAT across a variety of cooperative tasks, where agents are able to aggregate into explainable groups with less manual assistance and task priors.

Keywords:

Multi-agent

Sequence decision-making

Agent grouping

Cooperative tasks

Learning group collaboration for efficient multi-agent reinforcement learning

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