Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization

• URL: http://arxiv.org/abs/2607.00796v1
• Date: Wed, 01 Jul 2026 11:26:25 GMT
• Title: Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization
• Abstract summary: We propose a self-supervised Task-Relevant Representation Decoupling (T2RD) algorithm for Visual Reinforcement Learning (VRL)<n>T2RD achieves State-Of-The-Art (SOTA) generalization performance and sample efficiency in the DeepMind Control Suite and Robotic Manipulation tasks.
• Score: 36.5713429464476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Task-Relevant Representation Decoupling (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.

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