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A new prospective for Learning Automata: A machine learning approach
Wen Jiang1; Bin Li2; Shenghong Li1; Yuanyan Tang3; Chun Lung Philip Chen3
2016-05-05
Source PublicationNeurocomputing
ISSN0925-2312
Volume188Pages:319-325
Abstract

In the field of Learning Automata (LA), how to design faster learning algorithms has always been a key issue. Among solutions reported in the literature, the stochastic estimator reward-inaction learning automaton (SE), which belongs to the Maximum Likelihood estimator based LAs, has been recognized as the fastest ε-optimal LA. In this paper, we first point out the limitations of the traditional Maximum Likelihood Estimator (MLE) based LAs and then introduce Bayesian estimator based approach, which is demonstrated to be equivalent to Laplace smoothing of the traditional method, to overcome these limitations. The key idea is that the Bayesian estimator, which estimates the probability of selecting each action in the LA, aims to reconstruct Bernoulli distribution from sequential data, and is formalized based on exponential conjugate family so that the LA has a relatively simple format for easy implementation. In addition, we also indicate that this Bayesian estimator could be applied to update almost all existing MLE estimator based LAs. Based on the proposed Bayesian estimator, a new LA, known as Generalized Bayesian Stochastic Estimator (GBSE) LA, is presented and proved to be ε-optimal. Finally, extensive experimental results on benchmarks demonstrate that our proposed learning scheme is more efficient than the current best LA SE.

KeywordBayesian Estimator Learning Automata Maximum Likelihood Estimator Ε-optimal
DOIhttps://doi.org/10.1016/j.neucom.2015.04.125
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000375170000033
PublisherELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
3.Faculty of Science and Technology, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Wen Jiang,Bin Li,Shenghong Li,et al. A new prospective for Learning Automata: A machine learning approach[J]. Neurocomputing,2016,188:319-325.
APA Wen Jiang,Bin Li,Shenghong Li,Yuanyan Tang,&Chun Lung Philip Chen.(2016).A new prospective for Learning Automata: A machine learning approach.Neurocomputing,188,319-325.
MLA Wen Jiang,et al."A new prospective for Learning Automata: A machine learning approach".Neurocomputing 188(2016):319-325.
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