Measuring Visual Generalization in Continuous Control from Pixels

• URL: http://arxiv.org/abs/2010.06740v2
• Date: Fri, 27 Nov 2020 20:33:03 GMT
• Title: Measuring Visual Generalization in Continuous Control from Pixels
• Authors: Jake Grigsby, Yanjun Qi
• Abstract summary: Self-supervised learning and data augmentation have significantly reduced the performance gap between state and image-based reinforcement learning agents. We propose a benchmark that tests agents' visual generalization by adding graphical variety to existing continuous control domains. We find that data augmentation techniques outperform self-supervised learning approaches and that more significant image transformations provide better visual generalization.
• Score: 12.598584313005407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning and data augmentation have significantly reduced the performance gap between state and image-based reinforcement learning agents in continuous control tasks. However, it is still unclear whether current techniques can face a variety of visual conditions required by real-world environments. We propose a challenging benchmark that tests agents' visual generalization by adding graphical variety to existing continuous control domains. Our empirical analysis shows that current methods struggle to generalize across a diverse set of visual changes, and we examine the specific factors of variation that make these tasks difficult. We find that data augmentation techniques outperform self-supervised learning approaches and that more significant image transformations provide better visual generalization \footnote{The benchmark and our augmented actor-critic implementation are open-sourced @ https://github.com/QData/dmc_remastered)

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