Title: Plug-and-Play Object-Centric Reinforcement Learning via Masking
Primary area: reinforcement learning
Keywords: Deep Reinforcement Learning, Object-Centric RL, Feature Representation, Generalization

Abstract: Deep reinforcement learning agents, trained on raw pixel inputs, often fail to generalize beyond their training environments, relying on spurious correlations and irrelevant background details. To address this issue, object-centric agents have recently emerged. However, they require different representations tailored to the task specifications. 
Inspired by principles of cognitive science and Occam’s Razor, we introduce Object-Centric Attention via Masking (OCCAM), which selectively preserves task-relevant entities while filtering out irrelevant visual information, while taking advantage of the object-centric inductive bias.
Our empirical evaluations on Atari games demonstrate that OCCAM significantly improves robustness to novel perturbations while showing similar or improved performance compared to conventional pixel-based RL. These results suggest that structured abstraction can enhance generalization and, more importantly, highlight the importance and influence of input representations to our task.

TL;DR: Structured, object-centric abstraction, combined with the ease of CNNs, keeping task-relevant entities and discarding distractors to enhance generalization and highlight the influence of input representations.