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    Reinforcement learning· 2019

    Mastering Atari, Go, chess and shogi by planning with a learned model

    Julian Schrittwieser (first author) · 2019 · published 2020

    Read the original

    DOI 10.1038/s41586-020-03051-4arXiv 1911.08265OpenAlex W3118210634

    On the reading lists

    Why it matters

    Demonstrated powerful planning through learned dynamics without requiring known environment rules.

    Condensed from the expert lists' notes.

    Reading it

    Difficulty: Advanced

    Read this first: Mastering the game of Go without human knowledge.

    The record

    First released
    2019 (from arXiv 1911.08265)
    Published
    2020
    Citations
    2,800 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.511 · computed rank 225 · consensus 0.71, must-read 1, citations percentile 0.565 (2010s · reinforcement-learning), awards 0, existing lists 0. Method

    Editor's notes

    • Readability check: pass: MuZero: planning with a learned model, clearly told. (AI draft (Claude), not yet reviewed, 2026-10-29)

    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.