SOURCE-LINKED INTELLIGENCE
Robust and Efficient Communication for Multi-Agent Learning
Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated messages are both informative and robust to physical constraints remains a significant challenge. This paper introduces Multi-Agent Regularized Communication (MARC), a novel framework inspired by information-theoretic principles of conditional mutual information. MARC employs an attention-based architecture coupled with a unique message regularization mechanism designed to minimize uncertainty regarding future system states, thereby inducing the learnin
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T10:44:11.000Z
First collected: 2026-09-20T11:41:07.830Z. This is not the publication date.