JEV ARCADE

Ten ALE environments. One evaluation model on the joystick.

PLAYER 1 — JEV typesafe-ai/jev · frameskip 4

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env
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action_space
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observation_space
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What a DQN agent would see

t−3t−2t−1t

DeepMind's pipeline, reproduced live: the element-wise max of the last two frames (the 2600 flickered sprites on alternate frames), converted to luminance and resized to 84×84, with the last four stacked as the network input. JEV does not read this — it reads the structured state on the right — but this is the observation the published Atari agents were trained on.

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