Self-Consistent Model-based Adaptation for Visual Reinforcement Learning

• URL: http://arxiv.org/abs/2502.09923v1
• Date: Fri, 14 Feb 2025 05:23:56 GMT
• Title: Self-Consistent Model-based Adaptation for Visual Reinforcement Learning
• Authors: Xinning Zhou, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu,
• Abstract summary: Visual reinforcement learning agents face serious performance declines in real-world applications caused by visual distractions. Existing methods rely on fine-tuning the policy's representations with hand-crafted augmentations. We propose Self-Consistent Model-based Adaptation (SCMA), a novel method that fosters robust adaptation without modifying the policy.
• Score: 27.701421196547674
• License:
• Abstract: Visual reinforcement learning agents typically face serious performance declines in real-world applications caused by visual distractions. Existing methods rely on fine-tuning the policy's representations with hand-crafted augmentations. In this work, we propose Self-Consistent Model-based Adaptation (SCMA), a novel method that fosters robust adaptation without modifying the policy. By transferring cluttered observations to clean ones with a denoising model, SCMA can mitigate distractions for various policies as a plug-and-play enhancement. To optimize the denoising model in an unsupervised manner, we derive an unsupervised distribution matching objective with a theoretical analysis of its optimality. We further present a practical algorithm to optimize the objective by estimating the distribution of clean observations with a pre-trained world model. Extensive experiments on multiple visual generalization benchmarks and real robot data demonstrate that SCMA effectively boosts performance across various distractions and exhibits better sample efficiency.

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