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DROP: Dynamics-aware representation learning for offline reinforcement learning

delete2025-12-23
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PRE
AI
J
Jun Zheng
R
Runda Jia *
S
Shaoning Liu
R
Ranmeng Lin
D
Dakuo He
F
Fuli Wang
DOI:10.1016/j.asoc.2025.114525delete
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Abstract

Abstract

En 中文
• DROP learns dynamics-aware joint state–action representations for offline RL. • Jointly predicts rewards and next-state embeddings to capture transition structures. • Incorporates behavior cloning regularization directly into Q-function learning.

Journal

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

Organization

N
northeastern university
Scholars:
4.4K
Papers: 1.9K
Citations: 2
C
Criminal Investigation Police University of China
Scholars:
346
Papers: 210
Citations: 178