We present a novel method of learning style-agnostic representation using
both style transfer and adversarial learning in the reinforcement learning
framework. The style, here, refers to task-irrelevant details such as the color
of the background in the images, where generalizing the learned policy across
environments with different styles is still a challenge. Focusing on learning
style-agnostic representations, our method trains the actor with diverse image
styles generated from an inherent adversarial style perturbation generator,
which plays a min-max game between the actor and the generator, without
demanding expert knowledge for data augmentation or additional class labels for
adversarial training. We verify that our method achieves competitive or better
performances than the state-of-the-art approaches on Procgen and Distracting
Control Suite benchmarks, and further investigate the features extracted from
our model, showing that the model better captures the invariants and is less
distracted by the shifted style. The code is available at
this https URL

本文提出了一种新的学习风格无关表示的方法，利用强化学习框架下的风格迁移和对抗学习结合，通过内在的对抗风格扰动生成器来生成不同风格的图像样本，与 actor 进行极大 - 极小博弈，从而实现模型在不同环境下的泛化，并在流行的基准测试中验证了该方法的竞争性或优越性。