add code to ocatari paper

references.bib

@@ -764,6 +764,7 @@ @inproceedings{delfosse2024ocatari
 	year={2024},
   booktitle={Proceedings of the First Conference on Reinforcement Learning (RLC)},
 	url={https://arxiv.org/pdf/2306.08649},
+  crossref={https://github.com/k4ntz/OC_Atari},
 	Note = {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.},
 Keywords = {Deep Reinforcement Learning, Object-centric Deep Learning, Atari, Arcade Games, RAM Extraction method (REM), Vision Extraction method (VEM)}
 }