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    Reinforcement learning· 2020

    Conservative Q-Learning for Offline Reinforcement Learning

    Aviral Kumar (first author) · 2020

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    arXiv 2006.04779OpenAlex W3033324992

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    Why it matters

    Established conservative value estimation to counter distribution shift in offline reinforcement learning.

    Condensed from the expert lists' notes.

    The record

    First released
    2020 (from arXiv 2006.04779)
    Citations
    2,913 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.406 · computed rank 357 · consensus 0.42, must-read 0, citations percentile 0.8 (2020s · 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.