New Metric and Framework Enhance Multi-Agent Communication Under Delays

Researchers have developed a new metric called Communication Gain and Delay Cost (CGDC) to evaluate the effectiveness of communication in cooperative multi-agent reinforcement learning systems, particularly when messages experience cross-timestep delays. This metric helps quantify the trade-off between the value of information and the cost of its staleness. Based on CGDC, a novel actor-critic framework named CDCMA was proposed, which selectively requests messages, predicts future observations to mitigate misalignment, and fuses delayed messages using CGDC-guided attention. Experiments demonstrated that CDCMA improves performance, robustness, and generalization across various cooperative tasks. AI

IMPACT Introduces a new metric and framework to improve coordination in multi-agent systems facing communication delays, potentially enhancing performance in complex cooperative tasks.

RANK_REASON The cluster contains a research paper detailing a new metric and framework for multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• CDCMA
• Communication Gain and Delay Cost (CGDC)
• Cooperative Multi-Agent Reinforcement Learning
• Zihong Gao

• paper
• other

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