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Learning generalizable agents via self-supervised exploration

delete2025-07-03
delete0
PRE
AI
B
Baoxian Liang
L
Lihong Xu
Z
Z. Y. Deng
DOI：10.1016/j.neunet.2025.107787delete
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Abstract

Abstract

En 中文
• A self-supervised exploration framework to improve the generalization performance and sample efficiency of visual RL. • Devising a visual discrepancy inference module to learn features shared across different views. • Designing an exploration via distributional discrepancy module to identify the changed features. • Experiments are conducted on the DMControl-GB, Robotic Manipulation tasks and CARLA Autonomous Driving tasks.

Journal

Neural Networks cover
Neural Networks
IF:
6.3
Papers:
7.8K
Citations:
3.0W

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No organization information available