摘要翻译：
有界策略迭代是求解无限时域POMDPs的一种方法，它将策略表示为随机的有限状态控制器，通过线性规划调整每个节点的参数来迭代改进控制器。在原有的算法中，线性规划的规模和策略改进的复杂度依赖于每个节点的参数个数，而参数个数随着控制器的大小而增加。但在实际应用中，非零值节点的参数数往往很少，且不随控制器的大小而增长。基于这一观察，我们发展了一个有界策略迭代的版本，它利用了随机有限状态控制器的稀疏结构。在每次迭代中，它对策略的改进与原始算法相同，但具有更好的可伸缩性。
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英文标题：
《Sparse Stochastic Finite-State Controllers for POMDPs》
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作者：
Eric A. Hansen
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最新提交年份：
2012
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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。
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英文摘要：
  Bounded policy iteration is an approach to solving infinite-horizon POMDPs that represents policies as stochastic finite-state controllers and iteratively improves a controller by adjusting the parameters of each node using linear programming. In the original algorithm, the size of the linear programs, and thus the complexity of policy improvement, depends on the number of parameters of each node, which grows with the size of the controller. But in practice, the number of parameters of a node with non-zero values is often very small, and does not grow with the size of the controller. Based on this observation, we develop a version of bounded policy iteration that leverages the sparse structure of a stochastic finite-state controller. In each iteration, it improves a policy by the same amount as the original algorithm, but with much better scalability.
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PDF链接：
https://arxiv.org/pdf/1206.3263