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This is a Quantitative Study study (also classified as: Experimental Study).

September 29, 2025Open Access

Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning

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Incorporating actions and rewards during adaptation enriches data diversity for better learning.
Our Disentangled World Models framework successfully transfers semantic knowledge from distracting videos.
Pretraining action-free video prediction enhances the model's ability to learn semantic variations.
The proposed method demonstrates improved performance on various benchmarks in complex environments.

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This research introduces Disentangled World Models, improving semantic knowledge transfer and efficiency in reinforcement learning environments.

Wang et al. (2025) studied this question.

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This research introduces Disentangled World Models, improving semantic knowledge transfer and efficiency in reinforcement learning environments.