论文标题
图像增强就是您所需要的：正规化从像素的深入加固学习
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
论文作者
论文摘要
我们提出了一种简单的数据增强技术，该技术可以应用于标准的无模型增强学习算法，从而无需辅助损失或预训练即可直接从像素中进行健全的学习。该方法利用在计算机视觉任务中常用的输入扰动，以正规化值函数。现有的无模型方法，例如软参与者 - 批评（SAC），无法从图像像素中有效地训练深层网络。但是，我们的增强方法的添加大大提高了SAC的性能，从而使其能够在深媒体控制套件上达到最新的性能，超过模型的基于模型（Dreamer，Planet和Slac）方法以及最近提出的对比度学习（Curl）。我们的方法可以与任何无模型的增强学习算法结合使用，仅需要进行较小的修改。可以在https://sites.google.com/view/data-regularized-q上找到实现。
We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The approach leverages input perturbations commonly used in computer vision tasks to regularize the value function. Existing model-free approaches, such as Soft Actor-Critic (SAC), are not able to train deep networks effectively from image pixels. However, the addition of our augmentation method dramatically improves SAC's performance, enabling it to reach state-of-the-art performance on the DeepMind control suite, surpassing model-based (Dreamer, PlaNet, and SLAC) methods and recently proposed contrastive learning (CURL). Our approach can be combined with any model-free reinforcement learning algorithm, requiring only minor modifications. An implementation can be found at https://sites.google.com/view/data-regularized-q.
