TL;DR本文提出了一种名为 MoVie 的视觉模型策略适应方法，通过在测试期间实现视图泛化，无需任何明确的奖励信号和任何训练期间的修改，可显著提高目标任务的性能表现，这表明该方法在实际中应用于机器人技术具有巨大的潜力。
Abstract
visual reinforcement learning (RL) agents trained on limited views face
significant challenges in generalizing their learned abilities to unseen views.
This inherent difficulty is known as the problem of $\textit{view
generalization}$. In this work, we systematically categorize this fu