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• W2805745179 abstract "To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction. Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning." @default.
• W2805745179 created "2018-06-13" @default.
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• W2805745179 date "2018-06-08" @default.
• W2805745179 modified "2023-09-22" @default.
• W2805745179 title "Temporal Difference Variational Auto-Encoder" @default.
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• W2805745179 hasPublicationYear "2018" @default.
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