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新算法在对抗性线性CMDPs中实现最优遗憾

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的对偶算法，该算法在具有未知转移的周期性对抗性线性CMDPs中实现了最优的 $\widetilde{\mathcal{O}}(\sqrt{K})$ 遗憾和累积约束违反。这种新算法克服了先前方法（受限于 $\widetilde{\mathcal{O}}(K^{3/4})$）的局限性。该方法结合了自适应的FTRL（Follow the Regularized Leader）、收缩值估计和指数Lyapunov函数，消除了策略混合的需要，并确保策略参数与均匀集中兼容。 AI

排序理由 该集群包含一篇研究论文，详细介绍了一种用于特定类型决策过程的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

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新算法在对抗性线性CMDPs中实现最优遗憾

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该集群包含一篇研究论文，详细介绍了一种用于特定类型决策过程的新算法。[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) · Kihyun Yu, Honghao Wei, Dabeen Lee ·

    对抗性线性CMDPs的速率最优算法

    arXiv:2610.00927v1 Announce Type: new Abstract: We study episodic adversarial linear constrained Markov decision processes (CMDPs) with unknown transitions, where both the loss and constraint functions may vary adversarially across episodes. The best previous algorithm achieves $…

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