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    Reinforcement learning· 2018

    A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

    David Silver (first author) · 2018

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    DOI 10.1126/science.aar6404OpenAlex W2902907165

    On the reading lists

    Why it matters

    Generalized self-play and neural tree search across multiple challenging board games.

    Condensed from the expert lists' notes.

    The record

    First released
    2018 (from OpenAlex)
    Citations
    3,567 citations, as counted by OpenAlex (CC0)
    Named by
    both expert lists
    Score
    0.413 · computed rank 348 · consensus 0.425, must-read 0.5, citations percentile 0.674 (2010s · reinforcement-learning), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.