研究人员开发了一个新颖的基于 Lyapunov 的框架，用于分析弱耦合马尔可夫决策过程 (WCMDP) 和无休止赌徒 (RB) 学习的样本复杂度。与朴素的约简方法相比，该方法提供了更有效的学习近最优策略的方法，实现了多项式样本和计算复杂度。该框架建立了具有改进最优性差距的有限样本 PAC 保证，并引入了对线性规划松弛的细粒度扰动分析作为一项关键技术贡献。 AI
影响 引入了一个新颖的理论框架，可能导致更高效的 AI 学习算法，用于顺序决策问题。
排序理由 该集群包含一篇学术论文，详细介绍了用于分析特定类型马尔可夫决策过程样本复杂度的新理论框架。
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arXiv:2606.14095v1 Announce Type: cross Abstract: We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds a…
We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds as the state-action space is exponentially large in…
We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds as the state-action space is exponentially large in…