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• W3108977608 abstract "Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoencoders to encode a scene into a low-dimensional vector that can be used as a goal for an agent to discover new skills. Nevertheless, in compositional/multi-object environments it is difficult to disentangle all the factors of variation into such a fixed-length representation of the whole scene. We propose to use object-centric representations as a modular and structured observation space, which is learned with a compositional generative world model. We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills. These skills can be further combined to address compositional tasks like the manipulation of several different objects." @default.
• W3108977608 created "2020-12-07" @default.
• W3108977608 creator A5001474340 @default.
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• W3108977608 date "2020-11-29" @default.
• W3108977608 modified "2023-09-23" @default.
• W3108977608 title "Self-supervised Visual Reinforcement Learning with Object-centric Representations" @default.
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• W3108977608 hasPublicationYear "2020" @default.
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