Work

Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization

Year: 2026

Type: article

Abstract: Visual Reinforcement Learning (VRL) has achieved considerable success in solving control tasks. However, generalizing learned policies to new environments remains a major challenge, as agents often overfit to task-irrelevant features in the training environment. To solve this problem, we introduce the concept of decoupling observations into task-relevant and task-irrelevant representations. Building on this idea, we propose a self-supervised T ask- R elevant R epresentation D ecoupling (T2RD) algorithm for VRL. This algorithm consists of three components: task-relevant representation consistency , cross-reconstruction , and cross-dynamic prediction . The first two components achieve the decoupling of content and style features, but the resulting content representations are not necessarily task-relevant. To further refine task-relevant features from content representations, we design the third component that introduces dynamic prediction. T2RD achieves State-of-the-Art (SOTA) generalization performance and sample efficiency in the DeepMind Control Suite and Robotic Manipulation tasks.

Source: ACM Transactions on Multimedia Computing Communications and Applications

DOI: 10.1145/3820367