English(EN) Minimax Regret for Sobolev Kernel-Based Reinforcement Learning

新理论定义了核基强化学习的最优遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一个新的理论框架，用于理解核基强化学习中的minimax遗憾。该研究侧重于具有连续状态和动作空间以及对抗性初始状态的场景，并建立了依赖于Sobolev平滑参数和状态-动作维度的最优遗憾率。提出的算法Sobolev Optimal Value Iteration (SOVI)通过结合局部多项式逼近和共享RKHS结构来实现这些速率，解决了该领域的一个开放性问题。 AI

影响为核基强化学习建立了理论界限，可能指导未来在复杂环境中的算法开发。

排序理由该集群包含一篇详细介绍强化学习理论进展的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Haichen Hu, David Simchi-Levi · 2026-10-27 04:00

Sobolev核基强化学习的最小最大遗憾

arXiv:2610.34045v1 Announce Type: cross Abstract: We study the minimax regret rate for episodic kernel-based reinforcement learning, where the expected reward and transition density belong to a Sobolev reproducing kernel Hilbert space, with continuous state and action spaces, and…

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Sobolev核基强化学习的最小最大遗憾

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