Article

Mastering the game of Go without human knowledge

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Abstract

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.

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Acknowledgements

We thank A. Cain for work on the visuals; A. Barreto, G. Ostrovski, T. Ewalds, T. Schaul, J. Oh and N. Heess for reviewing the paper; and the rest of the DeepMind team for their support.

Author information

Author notes

    • David Silver
    • , Julian Schrittwieser
    •  & Karen Simonyan

    These authors contributed equally to this work.

Affiliations

  1. DeepMind, 5 New Street Square, London EC4A 3TW, UK.

    • David Silver
    • , Julian Schrittwieser
    • , Karen Simonyan
    • , Ioannis Antonoglou
    • , Aja Huang
    • , Arthur Guez
    • , Thomas Hubert
    • , Lucas Baker
    • , Matthew Lai
    • , Adrian Bolton
    • , Yutian Chen
    • , Timothy Lillicrap
    • , Fan Hui
    • , Laurent Sifre
    • , George van den Driessche
    • , Thore Graepel
    •  & Demis Hassabis

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Contributions

D.S., J.S., K.S., I.A., A.G., L.S. and T.H. designed and implemented the reinforcement learning algorithm in AlphaGo Zero. A.H., J.S., M.L. and D.S. designed and implemented the search in AlphaGo Zero. L.B., J.S., A.H., F.H., T.H., Y.C. and D.S. designed and implemented the evaluation framework for AlphaGo Zero. D.S., A.B., F.H., A.G., T.L., T.G., L.S., G.v.d.D. and D.H. managed and advised on the project. D.S., T.G. and A.G. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to David Silver.

Reviewer Information Nature thanks S. Singh and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

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    Reporting Summary

Zip files

  1. 1.

    Supplementary Data

    This zipped file contains the game records of self-play and tournament games played by AlphaGo Zero in .sgf format.