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English(EN) Online Learning in Stabilized Linear Dynamical Games with Adversarial Disturbances

多智能体对抗博弈中的在线学习新框架

作者 PulseAugur 编辑部 · [1 个来源] ·

本文提出了一个在稳定线性动力学博弈中的在线学习理论框架，该博弈设置涉及多个智能体在共享状态下进行交互，共享状态根据线性动力学演化并受到对抗性扰动的影响。该研究将对抗性在线控制扩展到多智能体系统，重点关注最小化自身凸损失的智能体。研究分析了采用带记忆在线梯度下降的智能体来适应其扰动-动作策略，并证明了次线性且接近最优的遗憾界限。在智能体具有共同利益的情况下，本文证明了该问题可以被构建为时变势能博弈，从而得到均衡跟踪保证。 AI

影响 为复杂多智能体系统中的对抗性在线学习提供了一个理论框架，可能影响博弈论和控制领域的未来研究。

排序理由 学术论文发表在arXiv上 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

多智能体对抗博弈中的在线学习新框架

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学术论文发表在arXiv上 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anas Barakat, John Lazarsfeld, Georgios Piliouras, Antonios Varvitsiotis ·

    具有对抗性扰动的稳定线性动力学博弈中的在线学习

    arXiv:2506.18814v3 Announce Type: replace Abstract: We study online learning in linear dynamical games where multiple strategic agents act on a shared state evolving according to a linear dynamical system subject to adversarial disturbances. This setting lies beyond both single-a…

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