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Such methods are grossly inefficient, often taking\norders of magnitudes more data than humans to achieve reasonable performance.\nWe propose Neural Episodic Control: a deep reinforcement learning agent that is\nable to rapidly assimilate new experiences and act upon them. Our agent uses a\nsemi-tabular representation of the value function: a buffer of past experience\ncontaining slowly changing state representations and rapidly updated estimates\nof the value function. 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