新的强化学习框架解决了未知参数的随机控制问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

本文介绍了一种新的强化学习框架，用于解决复杂的随机控制问题，其中漂移系数和运行奖励函数等关键参数是未知的。所提出的算法旨在学习最优值函数和反馈控制策略，具有理论基础并已证明收敛性。研究还探讨了涉及依赖于控制的扩散的特定情况，这需要先进的概率表示。 AI

影响引入了解决复杂随机控制问题的新算法，有可能推动需要不确定性下自适应决策的领域的研究。

排序理由该条目是发表在arXiv上的学术论文，详细介绍了针对特定类别控制问题的新的理论算法和收敛性证明。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Jin Ma, Gaozhan Wang, Jianfeng Zhang, Xunyu Zhou · 2026-09-15 04:00

学习解决具有未知漂移和运行奖励的随机控制问题：理论、算法与收敛性

arXiv:2609.14972v1 Announce Type: new Abstract: We study continuous-time and possibly high-dimensional stochastic control problems where drift coefficients and running reward functions are unknown. Due to these missing model primitives, we take the exploratory, reinforcement lear…