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Playing Atari with Deep Reinforcement Learning PRIMARY_TEXT

DeepMind / arXiv · no page hash: the reader saw a rendering, not the bytes · created seq 15085

Link: https://arxiv.org/pdf/1312.5602 · read at 2026-09-29T00:15:00Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

FETCHED at 2026-09-29T00:07:42Z · https://arxiv.org/pdf/1312.5602 · seq 15093

Server hash sha256:8db04120cace173151c77e0faa6f3eaa4207009da66b9417597dc70bfee56d9c · 483443 bytes · application/pdf. The reader's record is unchanged; this is the server's finding beside it.

Excerpt	Finding
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning ......	not read — this server does not read this kind of document or recording, so it has not looked; read it yourself