摘要翻译：
提出了一种度量有限马尔可夫决策过程(MDP)中状态相似性的度量方法。我们的度量标准是基于MDPs的双模拟概念，目的是解决折扣无限视界强化学习任务。这样的度量可用于聚合状态，以及更好地构造其他值函数近似器（例如，基于存储器的或最近邻近似器）。我们提供了将我们的度量距离与给定MDP中状态的最优值联系起来的界限。
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英文标题：
《Metrics for Finite Markov Decision Processes》
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作者：
Norman Ferns, Prakash Panangaden, Doina Precup
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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：
  We present metrics for measuring the similarity of states in a finite Markov decision process (MDP). The formulation of our metrics is based on the notion of bisimulation for MDPs, with an aim towards solving discounted infinite horizon reinforcement learning tasks. Such metrics can be used to aggregate states, as well as to better structure other value function approximators (e.g., memory-based or nearest-neighbor approximators). We provide bounds that relate our metric distances to the optimal values of states in the given MDP.
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PDF链接：
https://arxiv.org/pdf/1207.4114