English(EN) On the Hardness of Reinforcement Learning with Transition Look-Ahead

强化学习前瞻复杂度定义：1步可处理，2步以上为NP难

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

研究人员分析了当智能体能够基于动作序列预测未来状态时，强化学习（RL）的计算复杂度。他们开发了一种线性规划方法，可以在多项式时间内实现单步前瞻（$\ell=1$）的最优规划。然而，对于两步或更多步的前瞻深度（$\ell \geq 2$），该问题变为NP难，从而明确区分了此类RL规划在计算上可处理和不可处理的场景。 AI

影响定义了高级RL规划技术的计算限制，可能指导未来对高效算法的研究。

排序理由阐述计算复杂性理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Corentin Pla, Hugo Richard, Marc Abeille, Nadav Merlis, Vianney Perchet · 2026-10-27 04:00

关于带过渡前瞻的强化学习的难度

arXiv:2510.19372v3 Announce Type: replace Abstract: We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of $\ell$ actions before deciding its course of action. While such predictive i…

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关于带过渡前瞻的强化学习的难度

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