  Cognitive science and psychology suggest that object-centric representations
of complex scenes are a promising step towards enabling efficient abstract
reasoning from low-level perceptual features. Yet, most deep reinforcement
learning approaches only rely on pixel-based representations that do not
capture the compositional properties of natural scenes. For this, we need
environments and datasets that allow us to work and evaluate object-centric
approaches. In our work, we extend the Atari Learning Environments, the
most-used evaluation framework for deep RL approaches, by introducing OCAtari,
that performs resource-efficient extractions of the object-centric states for
these games. Our framework allows for object discovery, object representation
learning, as well as object-centric RL. We evaluate OCAtari's detection
capabilities and resource efficiency. Our source code is available at
github.com/k4ntz/OC_Atari.
