English(EN) Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions

无界随机博弈中学习均衡的新方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-14 04:00

研究人员开发了一种在新程序，用于学习具有高斯转移的无界随机博弈中的均衡。该方法利用单一数据轨迹来更新系统漂移的高斯过程模型，并求解一系列精炼的有限博弈。该程序被证明可以确保均衡选择的稳定性以及高斯转移模型的统一KL精度，从而在原始博弈中实现值收敛和近似纳什行为。 AI

影响为复杂博弈论场景引入了一种新颖的统计学习技术。

排序理由这是一篇发表在arXiv上的研究论文，详细介绍了一种新的统计学习程序。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Jonathan Anthony Mavroforas, Fred Roosta · 2026-10-14 04:00

高斯转移下无界随机博弈中的均衡学习

arXiv:2610.16149v1 Announce Type: new Abstract: Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds. We examine discounted general-sum stochastic games with compact action sets and an unknown …