English(EN) Offline Two-Player Zero-Sum Markov Games with KL Regularization

新框架致力于学习零和马尔可夫博弈中的纳什均衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-13 05:29

研究人员开发了一个名为正则化离线序贯均衡（ROSE）的新理论框架，用于学习离线双人零和马尔可夫博弈中的纳什均衡。该框架利用KL正则化来稳定学习并确保收敛，改进了现有通常依赖显式悲观主义的方法。还提出了一个实用的算法，序贯离线自对弈镜像下降（SOS-MD），该算法实现了快速收敛速度和消失的优化误差。 AI

影响引入了一个新颖的理论框架和算法，用于改进零和马尔可夫博弈中的学习，可能影响AI在策略决策领域的研究。

排序理由该集群描述了一篇新的学术论文，详细介绍了针对特定类型博弈论问题的理论框架和算法。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

SOS-MD

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

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-13 05:29

离线双人KL正则化零和马尔可夫博弈

We study the problem of learning Nash equilibria in offline two-player zero-sum Markov games. While existing approaches often rely on explicit pessimism to address distribution shift, we show that KL regularization alone suffices to stabilize learning and guarantee convergence. W…

本文如何被排名

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离线双人KL正则化零和马尔可夫博弈

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