摘要翻译：
近似动态规划已经成功地应用于许多领域，但它依赖于提供的少量近似特征来可靠地计算解。由于样本数量有限，大量丰富的特征集可能会导致现有算法过度拟合。我们用近似线性规划中的$L_1$正则化来解决这个缺点。由于该方法能够自动选择合适的特征丰富度，所以它的性能不会随着特征数量的增加而下降。这些结果依赖于正则化近似线性规划的新的更强的抽样界。我们还提出了一种计算效率高的同伦方法。对该方法的实证评价表明，该方法在简单的MDPs和标准基准问题上表现良好。
---
英文标题：
《Feature Selection Using Regularization in Approximate Linear Programs
  for Markov Decision Processes》
---
作者：
Marek Petrik, Gavin Taylor, Ron Parr, Shlomo Zilberstein
---
最新提交年份：
2010
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions reliably. Large and rich sets of features can cause existing algorithms to overfit because of a limited number of samples. We address this shortcoming using $L_1$ regularization in approximate linear programming. Because the proposed method can automatically select the appropriate richness of features, its performance does not degrade with an increasing number of features. These results rely on new and stronger sampling bounds for regularized approximate linear programs. We also propose a computationally efficient homotopy method. The empirical evaluation of the approach shows that the proposed method performs well on simple MDPs and standard benchmark problems.
---
PDF链接：
https://arxiv.org/pdf/1005.1860