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ENTROPIC MEASUREMENTS OF COMPLEXITY FOR FULLY OBSERVABLE MARKOV DECISION PROCESSES

机译:完全可观察马尔可夫决策过程复杂性的熵测量

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Complex dynamic systems are an active topic, and how to measure the complexity of dynamic decisions still remains to be an unresolved problem. In this paper, we apply Shannon's entropy as well as other entropy indices to measure the complexity for fully observable Markov decision processes, and extend the measurements of the complexity, uncertainty and unpredictability for Markov Chains to the measurements for Markov decision processes. We develop a methodology of information-theoretic complexity measurements for fully observable Markov decision processes.
机译:复杂的动态系统是一个活动主题,如何衡量动态决策的复杂性仍然是未解决的问题。在本文中,我们应用Shannon的熵以及其他熵索引来测量全面观察到的马尔可夫决策过程的复杂性,并延长Markov链条对马尔可夫决策过程的测量的复杂性,不确定性和不可预测性的测量。我们开发了一种信息理论复杂性测量方法,用于全部可观察的马尔可夫决策过程。

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