Applications of deep reinforcement learning — Alphago

Yingchen Liu

doi:10.54254/2755-2721/5/20230668

Research Article

Open access

Published on 31 May 2023

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Liu,Y. (2023). Applications of deep reinforcement learning — Alphago. Applied and Computational Engineering,5,637-641.

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Applications of deep reinforcement learning — Alphago

Yingchen Liu *^,1,

¹ Lassonde School Of Engineering, York University, Toronto, Ontario, Canada, M3J2S5

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/5/20230668

Abstract

With the progress of the times, the field of artificial intelligence (AI) has become one of the hottest fields in the 21st century. Currently, artificial intelligence is successfully used in the retail, financial, and medical industries. Especially in 2016, Google's DeepMind used deep reinforcement learning to train AlphaGo and defeated Lee Sedol, which propelled the field into the public eye. Most people are aware of artificial intelligence, but few understand it. This article will focus on analyzing the literature "Mastering the game of Go with deep neural networks and tree search" and other related articles to introduce the basics of deep reinforcement learning and AlphaGo. Finally, readers will understand how artificial intelligence can successfully imitate humans and defeat humans in Go.

Keywords

Deep Reinforcement Learning, Artificial Intelligence, Deep Learning, Reinforcement Learning, AlphaGo.

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References

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Cite this article

Liu,Y. (2023). Applications of deep reinforcement learning — Alphago. Applied and Computational Engineering,5,637-641.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.5

ISSN：2755-2721(Print) / 2755-273X(Online)

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