这篇论文教你用自监督方法解耦视觉特征，T2RD在泛化能力上比现有方法强出一截，做机器人和控制任务的值得看看。
T2RD提出通过自监督方式将观测分解为任务相关和任务无关表示，包含三个组件：任务相关表示一致性、交叉重建和交叉动态预测。前两个组件实现内容与风格特征解耦，第三个组件引入动态预测以进一步提取任务相关特征。在DeepMind Control Suite和Robotic Manipulation任务上，T2RD实现了SOTA的泛化性能和样本效率。
Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization
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.