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English(EN) Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs

Actor-Critic强化学习算法实现MDP的最优样本复杂度

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的arXiv论文探讨了Actor-Critic强化学习算法的进展。第一篇论文（后被撤回）提出，通过使用样本缓冲区和动量，单时间尺度Actor-Critic方法可以实现O(ε−2)的最优样本复杂度。第二篇论文为低秩MDP引入了一种新颖的乐观Actor-Critic算法，该算法仅依赖于策略评估，在无需计算成本高昂的预言机的情况下实现了改进的样本复杂度。 AI

影响 这些论文推进了对强化学习的理论理解，可能导致在复杂环境中更有效地训练智能体。

排序理由 两篇arXiv论文提出了强化学习算法的理论进展。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Actor-Critic强化学习算法实现MDP的最优样本复杂度

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两篇arXiv论文提出了强化学习算法的理论进展。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Navdeep Kumar, Tehila Dahan, Lior Cohen, Ananyabrata Barua, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor ·

    具有动量的最优单时间尺度Actor-Critic样本复杂度

    arXiv:2602.01505v2 Announce Type: replace Abstract: We establish an optimal sample complexity of $O(\epsilon^{-2})$ for obtaining an $\epsilon$-optimal global policy using a single-timescale actor-critic (AC) algorithm in infinite-horizon discounted Markov decision processes (MDP…

  2. arXiv cs.LG TIER_1 English(EN) · Ruiquan Huang, Donghao Li, Yingbin Liang, Jing Yang ·

    突破计算瓶颈：低秩MDP的可证明高效Actor-Critic方法

    arXiv:2605.01242v1 Announce Type: new Abstract: Reinforcement learning (RL) is a fundamental framework for sequential decision-making, in which an agent learns an optimal policy through interactions with an unknown environment. In settings with function approximation, many existi…

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