Self-supervised Visual Reinforcement Learning with Object-centric Representations

2021

Conference Paper

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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.

@inproceedings{zadaianchuk2021:visual-rl,
  title = {Self-supervised Visual Reinforcement Learning with Object-centric Representations},
  author = {Zadaianchuk*, Andrii and Seitzer*, Maximilian and Martius, Georg},
  booktitle = {9th International Conference on Learning Representations (ICLR 2021)},
  month = may,
  year = {2021},
  note = {*equal contribution},
  doi = {},
  month_numeric = {5}
}