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Regularizing Model Predictive Control for pixel-based long-horizon tasks

delete2025-06-10
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PRE
AI
F
Feng Zhang
Q
Qiang Hua
C
Chun-Ru Dong
DOI：10.1016/j.asoc.2025.113377delete
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Abstract

Abstract

En 中文
• A sample-efficient RMPC algorithm is proposed for pixel-based long-horizon tasks. • A regularization is incorporated into value function estimation and model learning. • RMPC achieves a 20.88% improvement and a 56.39% decrease in standard deviation.

Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

Organization

No organization information available