Conditional Optimality of Learning Automata with 2-state Bayesian Estimators
Conditional Optimality of Learning Automata with 2-state Bayesian Estimators
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DOI：
10.5687/sss.2019.223
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Motoshi Hara;Noriyo Kanayama;Toru Watanabe;S. Kato;H. Kamaya
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文献类型：
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作者：
Motoshi Hara;Noriyo Kanayama;Toru Watanabe;S. Kato;H. Kamaya
在β-型学习自动机的基础上，研究了一种具有两个状态贝叶斯估值器的新型学习自动机。具有多个贝叶斯估计的β-型学习自动机是我们的原创性工作，其方案已被证明在某些环境下是条件最优的。实验结果表明，与传统的学习自动机相比，β-型学习自动机具有更好的学习性能。然而，从内存使用和计算效率的角度来看，β类型的性能较差。因此，我们在以前的工作中提出了以两状态贝叶斯估值器为最小资源的β-型学习自动机。在这项研究中，我们证明了由两状态ffi估计组成的β-型LA的条件最优性的充分条件。
A novel learning automaton with 2 state Bayesian estimators, which is based on β -type learning automaton, is considered. The β -type learning automaton with multiple Bayesian estimators is our original work and the scheme of it has been proven to be conditionally optimal under some environments. Furthermore it was shown that β -type learning automaton exhibits superior learning behavior compared to conventional learning automata through several experimental results. However, the β -type one is inferior from the viewpoints of memory usage and computational efficiency. So, the β -type learning automaton which consists of 2-state Bayesian estimators as minimum resources has been proposed in our previous work. In this study, we show the sufficient condition for conditional optimality of the β -type LA which consists of 2-state Bayesian estimators.