新的Q学习算法为MDP提供无模型可达性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了Quasar，一种新颖的无模型算法，它使用Q学习在没有非终端最大结束组件（MECs）的马尔可夫决策过程（MDPs）中实现可达性规范的渐近收敛。这种方法消除了对显式估计转移概率的需求，而这是先前基于模型的方法的要求。与现有的最先进的基于模型的技术相比，Quasar显著减小了内存占用，并在定量验证基准集上展示了更快的收敛速度，标志着在规范引导强化学习方面迈出了实用性的一步。 AI

影响引入了一种更节省内存和样本的规范引导强化学习方法，可能支持更广泛的应用。

排序理由这是一篇详细介绍马尔可夫决策过程中强化学习新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Lu-Chin Chang, Suguman Bansal · 2026-10-02 04:00

Q-Learning for Reachability in MEC-Free MDPs

arXiv:2610.01781v1 Announce Type: new Abstract: Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence to optimal policies, but only through model-based methods that must explicitly e…

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Q-Learning for Reachability in MEC-Free MDPs

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