  Unsupervised object-centric representation (OCR) learning has recently drawn
attention as a new paradigm of visual representation. This is because of its
potential of being an effective pre-training 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 pre-training for image-based reinforcement learning
via empirical experiments. For systematic evaluation, we introduce a simple
object-centric visual RL benchmark and conduct experiments to answer questions
such as ``Does OCR pre-training improve performance on object-centric tasks?''
and ``Can OCR pre-training help with out-of-distribution generalization?''. Our
results provide empirical evidence for valuable insights into the effectiveness
of OCR pre-training for RL and the potential limitations of its use in certain
scenarios. Additionally, this study also examines the critical aspects of
incorporating OCR pre-training in RL, including performance in a visually
complex environment and the appropriate pooling layer to aggregate the object
representations.
