滚动抽样近似策略迭代的算法和界
Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration
摘要：
近年来，一些不带值函数的近似策略迭代方案被提出来，这些方案侧重于使用分类器的策略表示，并将策略学习作为一个监督学习问题来解决。使用这种方法找到好的策略不仅需要适当的分类器，还需要可靠的最佳动作示例，充分覆盖状态空间。到目前为止，很少有工作已经做了适当的覆盖计划和方法，以减少样本的复杂性，这种方法，特别是在连续状态空间。本文重点介绍了最简单的覆盖方案（状态空间上的离散网格），并在最简单的（以前常用的）卷展采样分配策略（在考虑中的每个状态下均匀分配样本）和几乎同样简单的方法（仅根据需要分配样本，需要的样本数量明显较少）之间进行了样本复杂性比较。
英文摘要：
Several approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem, have been proposed recently. Finding good policies with such methods requires not only an appropriate classifier, but also reliable examples of best actions, covering the state space sufficiently. Up to this time, little work has been done on appropriate covering schemes and on methods for reducing the sample complexity of such methods, especially in continuous state spaces. This paper focuses on the simplest possible covering scheme (a discretized grid over the state space) and performs a sample-complexity comparison between the simplest (and previously commonly used) rollout sampling allocation strategy, which allocates samples equally at each state under consideration, and an almost as simple method, which allocates samples only as needed and requires significantly fewer samples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
