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新的CMDP算法绕过Slater条件以提高性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为在线片段式约束马尔可夫决策过程（CMDPs）开发了一种新算法，该算法改进了现有方法。该算法处理随机和对抗性约束，而无需Slater条件，这是一个重大的进步，允许在不存在严格可行解的情况下进行设置。它在懊悔和约束违反保证方面取得了改进，甚至解决了正约束违反问题，并在对抗性场景中提供了相对于无约束最优值的次线性 alpha-懊悔。该算法的有效性已通过合成实验得到证明。 AI

影响 这项研究推进了约束下决策的理论理解和算法能力，可能影响需要在复杂、不确定环境中实现稳健性能的AI系统。

排序理由 详细介绍CMDP新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的CMDP算法绕过Slater条件以提高性能

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详细介绍CMDP新算法的学术论文。[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) · Francesco Emanuele Stradi, Eleonora Fidelia Chiefari, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti ·

    在线CMDP中超越Slater条件：随机与对抗约束

    arXiv:2509.20114v3 Announce Type: replace Abstract: We study \emph{online episodic Constrained Markov Decision Processes} (CMDPs) under both stochastic and adversarial constraints. We provide a novel algorithm whose guarantees greatly improve those of the state-of-the-art best-of…

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