OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments

Quentin Delfosse, Jannis Blüml, Bjarne Gregori, Sebastian Sztwiertnia, Kristian Kersting

Primary Area: datasets and benchmarks

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Keywords: Object Centric, Reinforcement Learning, Framework, Environments, Object Detection, Object Discovery

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TL;DR: We introduce an object centric framework, that extracts objects-centric states of different games of the famous Atari Learning Environment RL benchmark.

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 rely on only 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. We present OCAtari, a set of environment that provides object-centric state representations of Atari games, the most-used evaluation framework for deep RL approaches. OCAtari also allows for RAM state manipulations of the games to change and create specific or even novel situations. Our source code is available at
https://anonymous.4open.science/r/OCAtari-52B9 .

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Submission Number: 1860