摘要翻译:
本文提出了一种度量具有无限多个状态的马尔可夫决策过程(包括具有连续状态空间的马尔可夫决策过程)状态相似性的度量方法。这样的度量为MDP的双模拟概念提供了一个稳定的定量模拟,并且适合于MDP近似中使用。我们证明了与贴现无限时域规划任务相关的最优值函数随着度量距离的变化而连续变化。
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英文标题:
《Metrics for Markov Decision Processes with Infinite State Spaces》
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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 state similarity in Markov decision processes (MDPs) with infinitely many states, including MDPs with continuous state spaces. Such metrics provide a stable quantitative analogue of the notion of bisimulation for MDPs, and are suitable for use in MDP approximation. We show that the optimal value function associated with a discounted infinite horizon planning task varies continuously with respect to our metric distances.
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PDF链接:
https://arxiv.org/pdf/1207.1386