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首页> 外文期刊>Kybernetika >MEAN-VARIANCE OPTIMALITY FOR SEMI-MARKOV DECISION PROCESSES UNDER FIRST PASSAGE CRITERIA
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MEAN-VARIANCE OPTIMALITY FOR SEMI-MARKOV DECISION PROCESSES UNDER FIRST PASSAGE CRITERIA

机译:首次通过标准的半马尔可夫决策过程的均方差最优

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摘要

This paper deals with a first passage mean-variance problem for semi-Markov decision processes in Borel spaces. The goal is to minimize the variance of a total discounted reward up to the system's first entry to some target set, where the optimization is over a class of policies with a prescribed expected first passage reward. The reward rates are assumed to be possibly unbounded, while the discount factor may vary with states of the system and controls. We first develop some suitable conditions for the existence of first passage mean-variance optimal policies and provide a policy improvement algorithm for computing an optimal policy. Then, two examples are included to illustrate our results. At last, we show how the results here are reduced to the cases of discrete-time Markov decision processes and continuous-time Markov decision processes.
机译:本文讨论了Borel空间中半Markov决策过程的第一遍均值-方差问题。目标是最大程度地减少直到系统首次进入某个目标集为止的总折扣奖励的方差,在该目标集上,优化是针对具有规定的预期首次通过奖励的一类策略。假定奖励率可能不受限制,而折扣因数可能会随系统和控件的状态而变化。我们首先为首次通过均方差最优策略的存在开发了一些合适的条件,并提供了一种用于计算最优策略的策略改进算法。然后,包括两个示例以说明我们的结果。最后,我们展示了如何将结果简化为离散时间马尔可夫决策过程和连续时间马尔可夫决策过程的情况。

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