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新算法优化带任意延迟的游戏学习

研究人员开发了一种名为多锚点乐观在线镜像下降(MAO-OMD)的新算法,用于在具有延迟反馈的零和游戏中进行学习。该算法在平滑游戏中的自玩(self-play)方面实现了最优的快速收敛率,同时对任何对手都提供了 minimax 最优保证。该算法在具有未知反馈延迟的平滑零和游戏中特别有效,与标准方法相比,其遗憾界限得到了改进。 AI

影响 引入了一种优化带延迟反馈游戏学习的新算法,可能影响人工智能在战略决策领域的研究。

排序理由 该集群包含一篇详细介绍新博弈论算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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

新算法优化带任意延迟的游戏学习

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该集群包含一篇详细介绍新博弈论算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Emmanuel Esposito, Andrew Jacobsen, Hao Qiu, Alexander Ryabchenko, Mengxiao Zhang ·

    具有任意延迟的游戏中的最优学习:自玩和对抗性对手的最佳组合

    arXiv:2611.07773v1 Announce Type: cross Abstract: We study learning in zero-sum games with delayed feedback. We introduce Multi-Anchor Optimistic Online Mirror Descent (MAO-OMD), a single algorithm which obtains optimal fast rates in self-play on smooth games, while simultaneousl…