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新理论推进连续随机控制中的Q学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员在arXiv上发表了一篇论文，详细介绍了Q学习（强化学习中的一个基本算法）的理论进展。该研究侧重于连续状态和动作空间中Q学习的数学基础，特别是分析了贝尔曼最优性目标。该论文提出了一种专门针对问题混合正则性属性的DeepONet架构，并推导了近似界限，强调了随着时间步长接近零时，刚度和复杂性之间的权衡。 AI

影响 推进了对强化学习算法的理论理解，可能为未来的实际应用提供信息。

排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了Q学习的理论进展。

在 arXiv cs.AI 阅读 →

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新理论推进连续随机控制中的Q学习

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该集群包含一篇在arXiv上发表的研究论文，详细介绍了Q学习的理论进展。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Qian Qi ·

    深度Q学习在H\"older空间上

    arXiv:2606.16846v1 Announce Type: cross Abstract: We study the operator-theoretic core of Q-learning in continuous-time stochastic control with continuous states and actions. In value-based reinforcement learning, each Q-learning or DQN update is built from a Bellman optimality t…

  2. arXiv cs.AI TIER_1 English(EN) · Qian Qi ·

    Hölder 空间上的深度 Q-学习

    We study the operator-theoretic core of Q-learning in continuous-time stochastic control with continuous states and actions. In value-based reinforcement learning, each Q-learning or DQN update is built from a Bellman optimality target; our analysis isolates this target in a diff…

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