Self-supervised Visual Reinforcement Learning with Object-centric Representations

• URL: http://arxiv.org/abs/2011.14381v1
• Date: Sun, 29 Nov 2020 14:55:09 GMT
• Title: Self-supervised Visual Reinforcement Learning with Object-centric Representations
• Authors: Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
• Abstract summary: We propose to use object-centric representations as a modular and structured observation space. 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.
• Score: 11.786249372283562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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