Photonic Approach to Reinforcement Learning-Reference-Cited by-同舟云学术

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Photonic Approach to Reinforcement Learning

Published:2022-07-03 Issue: Volume: Page:
ISSN:
Container-title:2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC)
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Author:

Naruse Makoto¹,Uchida Atsushi²,Uchiyama Kazuharu³,Akahane Kouichi⁴

Affiliation:

1. Graduate School of Information Science and Technology, University of Tokyo,Department of Information Physics and Computing,Tokyo,Japan,113-8656

2. Saitama University,Department of Information and Computer Sciences,Saitama,Japan,338-8570

3. University of Yamanashi,Yamanashi,Japan,400-8511

4. National Institute of Information and Communications Technology,Tokyo,Japan,184-8795

Funder

Japan Society for the Promotion of Science

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9849706/9849712/09849932.pdf?arnumber=9849932

Reference23 articles.

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

2. Analysis on Effectiveness of Surrogate Data-Based Laser Chaos Decision Maker

3. Theory of Acceleration of Decision Making by Correlated Times Sequences;okada;ArXiv,0

4. Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series

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