一项新的研究探讨了强化学习中用于控制任务的贝尔曼残差最小化方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

本文介绍了将贝尔曼残差最小化应用于马尔可夫决策问题策略优化的基础性研究成果。虽然动态规划更为常见，但贝尔曼残差最小化在函数逼近方面具有稳定的收敛性等优势。该研究侧重于将此方法扩展到控制任务，而控制任务的探索程度不如策略评估。 AI

影响推进了对控制算法的理论理解，可能提高强化学习的稳定性。

排序理由这是一篇发表在arXiv上的研究论文，详细介绍了马尔可夫决策问题的控制算法的理论进展。

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arXiv cs.LG TIER_1 English(EN) · Donghwan Lee, Hyukjun Yang · 2026-04-28 04:00

Bellman 残差最小化在控制中的应用：几何、平稳性和收敛性

arXiv:2601.18840v3 Announce Type: replace Abstract: Markov decision problems are most commonly solved via dynamic programming. Another approach is Bellman residual minimization, which directly minimizes the squared Bellman residual objective function. However, compared to dynamic…