Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization
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Computer Science > Machine Learning
Title:Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization
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.
| Comments: | 23 pages, 13 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.00796 [cs.LG] |
| (or arXiv:2607.00796v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00796
arXiv-issued DOI via DataCite (pending registration)
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