OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments

arXiv:2306.08649v2 Announce Type: replace
Abstract: 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 …

abstract arxiv cognitive cognitive science cs.ai cs.cv cs.lg enabling environments features low natural pixel psychology reasoning reinforcement reinforcement learning science type