Arm order recognition in multi-armed bandit problem with laser chaos time series

Abstract By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects th...

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Autores principales:	Naoki Narisawa, Nicolas Chauvet, Mikio Hasegawa, Makoto Naruse
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/82e24ef171b342e0baa70ab605b865cc
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spelling	oai:doaj.org-article:82e24ef171b342e0baa70ab605b865cc2021-12-02T13:20:04ZArm order recognition in multi-armed bandit problem with laser chaos time series10.1038/s41598-021-83726-82045-2322https://doaj.org/article/82e24ef171b342e0baa70ab605b865cc2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83726-8https://doaj.org/toc/2045-2322Abstract By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of the order of arms in terms of reward expectations is not achievable. Here, we present an algorithm where the degree of exploration is adaptively controlled based on confidence intervals that represent the estimation accuracy of reward expectations. We have demonstrated numerically that our approach did improve arm order recognition accuracy significantly, along with reduced dependence on reward environments, and the total reward is almost maintained compared with conventional MAB methods. This study applies to sectors where the order information is critical, such as efficient allocation of resources in information and communications technology.Naoki NarisawaNicolas ChauvetMikio HasegawaMakoto NaruseNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naoki Narisawa Nicolas Chauvet Mikio Hasegawa Makoto Naruse Arm order recognition in multi-armed bandit problem with laser chaos time series
description	Abstract By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of the order of arms in terms of reward expectations is not achievable. Here, we present an algorithm where the degree of exploration is adaptively controlled based on confidence intervals that represent the estimation accuracy of reward expectations. We have demonstrated numerically that our approach did improve arm order recognition accuracy significantly, along with reduced dependence on reward environments, and the total reward is almost maintained compared with conventional MAB methods. This study applies to sectors where the order information is critical, such as efficient allocation of resources in information and communications technology.
format	article
author	Naoki Narisawa Nicolas Chauvet Mikio Hasegawa Makoto Naruse
author_facet	Naoki Narisawa Nicolas Chauvet Mikio Hasegawa Makoto Naruse
author_sort	Naoki Narisawa
title	Arm order recognition in multi-armed bandit problem with laser chaos time series
title_short	Arm order recognition in multi-armed bandit problem with laser chaos time series
title_full	Arm order recognition in multi-armed bandit problem with laser chaos time series
title_fullStr	Arm order recognition in multi-armed bandit problem with laser chaos time series
title_full_unstemmed	Arm order recognition in multi-armed bandit problem with laser chaos time series
title_sort	arm order recognition in multi-armed bandit problem with laser chaos time series
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/82e24ef171b342e0baa70ab605b865cc
work_keys_str_mv	AT naokinarisawa armorderrecognitioninmultiarmedbanditproblemwithlaserchaostimeseries AT nicolaschauvet armorderrecognitioninmultiarmedbanditproblemwithlaserchaostimeseries AT mikiohasegawa armorderrecognitioninmultiarmedbanditproblemwithlaserchaostimeseries AT makotonaruse armorderrecognitioninmultiarmedbanditproblemwithlaserchaostimeseries
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Arm order recognition in multi-armed bandit problem with laser chaos time series

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