Papers › Rotation, Translation, and Cropping for Zero-Shot Generalization

Rotation, Translation, and Cropping for Zero-Shot Generalization

27 Jan 2020arXiv:2001.09908archive 2025-07-28

Chang Ye, Ahmed Khalifa, Philip Bontrager, Julian Togelius

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.

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Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)Translation Zero-shot Generalization

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