When and Why Is Pretraining Object-Centric Representations Good for Reinforcement Learning?

Unsupervised object-centric representation (OCR) learning has recently been drawing a lot of attention as a new paradigm of visual representation. This is because of its potential of being an effective pretraining technique for various downstream tasks in terms of sample efficiency, systematic generalization, and reasoning. Although image-based reinforcement learning (RL) is one of the most important and thus frequently mentioned such downstream tasks, the benefit in RL has surprisingly not been investigated systematically thus far. Instead, most of the evaluations have focused on rather indirect metrics such as segmentation quality and object property prediction accuracy. In this paper, we investigate the effectiveness of OCR pretraining for image-based reinforcement learning via empirical experiments. For systematic evaluation, we introduce a simple object-centric visual RL benchmark and verify a series of hypotheses answering questions such as "Does OCR pretraining provide better sample efficiency?", "Which types of RL tasks benefit most from OCR pretraining?", and "Can OCR pretraining help with out-of-distribution generalization?". The results suggest that OCR pretraining is particularly effective in tasks where the relationship between objects is important, improving both task performance and sample efficiency when compared to single-vector representations. Furthermore, OCR models facilitate generalization to out-of-distribution tasks such as changing the number of objects or the appearance of the objects in the scene.