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Visualizing and Understanding Policy Networks of Computer Go

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 11012)


In May 2017, the application of deep learning to the game “Go” enjoyed a tremendous victory when the AlphaGo computer program beat one of the top professional players. However, there is no clear understanding of why deep learning elicits such strong performance. In this paper, we introduce visualization techniques used in image recognition to investigate the functions of the intermediate layers and operations of the Go policy network. Used as a diagnostic tool, these visualization techniques allow us to understand what happens during the training of policy networks. We also introduce a visualization technique that performs a sensitivity analysis of the classifier output by occluding portions of the input Go board, revealing which parts of the board are important for predicting the next move.


  • Deep learning
  • Computer Go
  • Visualization

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  • DOI: 10.1007/978-3-319-97304-3_20
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Correspondence to Yuanfeng Pang .

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Pang, Y., Ito, T. (2018). Visualizing and Understanding Policy Networks of Computer Go. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97303-6

  • Online ISBN: 978-3-319-97304-3

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