What question did this study set out to answer?

The aim is to improve generalization of visual reinforcement learning policies by decoupling task-relevant from task-irrelevant features.

July 2, 2026

Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization

Key Points

The aim is to improve generalization of visual reinforcement learning policies by decoupling task-relevant from task-irrelevant features.
Introduced the T2RD algorithm focusing on decoupling observations into relevant and irrelevant features.
Implemented three main components: task-relevant representation consistency, cross-reconstruction, and dynamic prediction.
Evaluated performance on DeepMind Control Suite and Robotic Manipulation tasks.
Achieved state-of-the-art generalization performance in tested environments.
Demonstrated improved sample efficiency compared to traditional methods.

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.

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