DRMAC shows that after agents exchange messages, the integrated message embeddings still contain redundant and decision-irrelevant dimensions, and it reduces both with a Barlow-Twins-style loss plus a meta-learned dimensional mask.
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Revisiting Communication Efficiency in Multi-Agent Reinforcement Learning from the Dimensional Analysis Perspective
DRMAC shows that after agents exchange messages, the integrated message embeddings still contain redundant and decision-irrelevant dimensions, and it reduces both with a Barlow-Twins-style loss plus a meta-learned dimensional mask.