English(EN) OSCC: Certified Observation-Safe Coupling Optimization for Gradient-Noise Control in Imperfect-Information Learning

新的OSCC框架增强了不完美信息学习中的梯度噪声控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一个名为OSCC（观测安全反事实耦合）的新框架，以改善不完美信息学习环境中的梯度噪声控制。该框架通过确保安全和优化策略梯度几何来解决标准通用随机数构造的问题。OSCC的CP-GRPO实例证明了回返对比度方差的显著降低，在比较中实现了55.87%的降幅。 AI

影响为复杂环境中学习算法的稳定性和效率的提高引入了一种新颖的方法。

排序理由这是一篇详细介绍新框架及其应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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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arXiv cs.AI TIER_1 English(EN) · Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Rui Chen, Daren Zha · 2026-10-19 04:00

OSCC：用于不完美信息学习中梯度噪声控制的认证观测安全耦合优化

arXiv:2609.33543v2 Announce Type: replace Abstract: Coupled rollouts can reduce the noise of counterfactual action comparisons, but two issues prevent standard common-random-number constructions from serving as a general learning primitive in imperfect-information environments. F…