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New Transformer Framework Bridges MARL to SARL for Enhanced Coordination

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了共识多智能体Transformer（CMAT），一个旨在弥合合作多智能体强化学习（MARL）与分层单智能体强化学习（SARL）的新型框架。CMAT使用Transformer编码器处理大型联合观测空间，并通过分层决策机制解决广泛的联合动作空间问题。该机制自回归地生成一个高级共识向量，使智能体能够在潜在空间中就策略达成一致，并同时生成与顺序无关的动作。在StarCraft II、Multi-Agent MuJoCo和Google Research Football上的实验表明，CMAT的性能优于现有的集中式和顺序式MARL方法。 AI

影响 引入了一种改进多智能体强化学习系统中协调和训练稳定性方面的新方法。

排序理由 详细介绍新框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

New Transformer Framework Bridges MARL to SARL for Enhanced Coordination

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详细介绍新框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 Nederlands(NL) · Zijian Zhao, Jing Gao, Sen Li ·

    将MARL桥接到SARL：一种无序多智能体Transformer通过潜在共识实现

    arXiv:2604.13472v2 Announce Type: replace-cross Abstract: Cooperative multi-agent reinforcement learning (MARL) is widely used to address large joint observation and action spaces by decomposing a centralized control problem into multiple interacting agents. However, such decompo…

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