研究人员为强化学习中的离策略Actor-Critic方法建立了一个新的理论样本复杂度保证。该论文证明了在最少假设下找到$\\epsilon$-最优策略的第一个$\\tilde{\\mathcal{O}}(\\epsilon^{-2})$样本复杂度，具体要求仅为不可约马尔可夫链。这一成就与先前需要嵌套循环更新或更强的、依赖于算法的策略假设的工作形成对比。 AI
影响 为强化学习算法树立了新的理论基准，有望提高未来应用的样本效率。
排序理由 详细介绍强化学习算法理论进展的学术论文。
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In this paper, we establish last-iterate convergence rates for off-policy actor--critic methods in reinforcement learning. In particular, under a single-loop, single-timescale implementation and a broad class of policy updates, including approximate policy iteration and natural p…
arXiv:2605.13639v1 Announce Type: cross Abstract: In this paper, we establish last-iterate convergence rates for off-policy actor--critic methods in reinforcement learning. In particular, under a single-loop, single-timescale implementation and a broad class of policy updates, in…