Scalable photonic reinforcement learning by time-division multiplexing of laser chaos

Scientific Reports
Makoto NaruseAtsushi Uchida

Abstract

Reinforcement learning involves decision-making in dynamic and uncertain environments and constitutes a crucial element of artificial intelligence. In our previous work, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers can be used to efficiently solve the two-armed bandit problem, which requires decision-making concerning a class of difficult trade-offs called the exploration-exploitation dilemma. However, only two selections were employed in that research; hence, the scalability of the laser-chaos-based reinforcement learning should be clarified. In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time series. The experimental demonstrations in which bandit problems with up to 64 arms were successfully solved are presented where laser chaos time series significantly outperforms quasiperiodic signals, computer-generated pseudorandom numbers, and coloured noise. Detailed analyses are also provided that include performance comparisons among laser chaos signals generated in different physical conditions, which coincide with the diffusivity inherent in the time ...Continue Reading

References

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Aug 3, 2011·The Review of Scientific Instruments·M D EisamanS V Polyakov
Apr 12, 2013·Reports on Progress in Physics·Makoto NaruseMotoichi Ohtsu
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Aug 19, 2015·Scientific Reports·Makoto NaruseSong-Ju Kim
Nov 5, 2016·Science·Takahiro InagakiHiroki Takesue
Dec 9, 2016·Scientific Reports·Song-Ju KimTakuma Akimoto
Aug 20, 2017·Scientific Reports·Makoto NaruseSong-Ju Kim
Oct 21, 2017·Nature·David SilverDemis Hassabis

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Citations

Jul 3, 2019·Scientific Reports·Ryutaro HommaSatoshi Sunada
Sep 12, 2019·Scientific Reports·Makoto NaruseAtsushi Uchida
Aug 24, 2019·Scientific Reports·Nicolas ChauvetMakoto Naruse
Nov 26, 2020·Scientific Reports·Nicolas ChauvetMakoto Naruse
Jan 1, 2021·Optics Express·Takatomo MihanaAtsushi Uchida
Feb 26, 2021·Scientific Reports·Naoki NarisawaMakoto Naruse

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Software Mentioned

RAND
MATLAB

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