Learning generalizable agents via self-supervised exploration

Baoxian Liang

doi:10.1016/j.neunet.2025.107787

2025-07-03

PRE

Baoxian Liang

Lihong Xu

Z. Y. Deng

DOI：10.1016/j.neunet.2025.107787

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摘要

En 中文

• 一种自监督探索框架，用于提升视觉强化学习的泛化性能和样本效率。 • 设计一个视觉差异推理模块，以学习不同视图间的共享特征。 • 设计一个通过分布差异进行探索的模块，以识别发生变化的特征。 • 在DMControl-GB、机器人操作任务和CARLA自动驾驶任务上进行了实验。

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