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We propose a simple data augmentation technique that can be applied tostandard model-free reinforcement learning algorithms, enabling robust learningdirectly from pixels without the need for auxiliary losses or pre-training. Theapproach leverages input perturbations commonly used in computer vision tasksto regularize the value function. Existing model-free approaches, such as SoftActor-Critic (SAC), are not able to train deep networks effectively from imagepixels. However, the addition of our augmentation method dramatically improvesSAC's performance, enabling it to reach state-of-the-art performance on theDeepMind control suite, surpassing model-based (Dreamer, PlaNet, and SLAC)methods and recently proposed contrastive learning (CURL). Our approach can becombined with any model-free reinforcement learning algorithm, requiring onlyminor modifications. An implementation can be found athttps://sites.google.com/view/data-regularized-q.

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels