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Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
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Humans can quickly learn new tasks from just a handful of examples by preserving rich representations of experience [1].
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Online Allocationand Learninginthe Presenceof Strategic Agents
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Notethatthere(2) (see[4]) F is Therefore ( ) asablack-box distributionหF.
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Sustainable Online Reinforcement Learningfor Auto-bidding
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Definition (Inconsistencies between Specifically cannot constraint 2 ofthe3, which VAS second advertisers mark Essentially transitions performance R) A/Btest the RAS.