The content discusses the importance of reaching consensus in cooperative multi-agent reinforcement learning. The proposed MAGI framework introduces a model-based consensus mechanism to guide agents towards valuable future states. Results demonstrate MAGI's superiority in both sample efficiency and performance across various environments.
The paper highlights the challenges in multi-agent coordination and presents a novel approach, MAGI, to address these issues effectively. By modeling future state distributions and generating common goals, MAGI enhances cooperation among agents. Experimental results showcase the success of MAGI in improving multi-agent coordination and performance.
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by Liangzhou Wa... at arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.03172.pdfDeeper Inquiries