论文标题
旋转，翻译和裁剪以进行零拍的概括
Rotation, Translation, and Cropping for Zero-Shot Generalization
论文作者
论文摘要
深度强化学习（DRL）在带有视觉输入的域上表现出令人印象深刻的性能，尤其是各种游戏。但是，代理通常在固定环境中进行训练，例如固定数量的级别。越来越多的证据表明，这些受过训练的模型无法推广到他们受过训练的环境的轻微变化。本文提出了以下假设：缺乏概括的部分是由于输入表示，并探讨了旋转，种植和翻译如何增加通用性。我们表明，从GVGAI框架中，裁剪，翻译和旋转的观察结果可以在看不见的二维街机游戏中获得更好的概括。在人工设计和程序生成的水平上都评估了代理的一般性。
Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games. However, the agent is usually trained on a fixed environment, e.g. a fixed number of levels. A growing mass of evidence suggests that these trained models fail to generalize to even slight variations of the environments they were trained on. This paper advances the hypothesis that the lack of generalization is partly due to the input representation, and explores how rotation, cropping and translation could increase generality. We show that a cropped, translated and rotated observation can get better generalization on unseen levels of two-dimensional arcade games from the GVGAI framework. The generality of the agents is evaluated on both human-designed and procedurally generated levels.
