A fuzzy approach to Markov decision processes with uncertain transition probabilities

M. Kurano; M. Yasuda; J. Nakagami; Y. Yoshida

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A fuzzy approach to Markov decision processes with uncertain transition probabilities

机译：具有不确定转移概率的马尔可夫决策过程的模糊方法

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摘要

In this paper, Markov decision models with uncertain transition matrices, which allow a matrix to fluctuate at each step in time, is described by the use of fuzzy sets. We find a Pareto optimal policy maximizing the infinite horizon fuzzy expected discounted reward (FEDR) over all stationary policies under some partial order. The Pareto optimal policies are characterized by maximal solutions of an optimal inclusion including efficient set-functions. As a numerical example, a machine maintenance problem is considered.

机译：在本文中，使用模糊集描述了具有不确定转移矩阵的Markov决策模型，该模型允许矩阵在每个时间步长波动。我们找到了一个帕累托最优策略，该策略在某些偏序下最大化了所有平稳策略上的无限远景模糊期望折现奖励（FEDR）。帕累托最优策略的特征在于包括有效集合函数在内的最优包含的最大解。作为数值示例，考虑了机器维护问题。

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来源
《Fuzzy sets and systems》 |2006年第19期|p.2674-2682|共9页
作者
M. Kurano; M. Yasuda; J. Nakagami; Y. Yoshida;
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作者单位

Faculty of Education, Chiba University, Chiba 263-8522, Japan;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类模糊数学;
关键词
fuzzy analysis; markov decision process; pareto optimal; optimal inclusion;

机译：模糊分析;马尔可夫决策过程;最优;最优包含;

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A fuzzy approach to Markov decision processes with uncertain transition probabilities

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