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

28 Sept 2020, 15:49 (edited 10 Feb 2022)ICLR 2021 SpotlightReaders: Everyone
  • Abstract: 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 transform input examples, as well as regularizing the value function and policy. 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 (Hafner et al., 2019; Lee et al., 2019; Hafner et al., 2018) methods and recently proposed contrastive learning (Srinivas et al., 2020). Our approach, which we dub DrQ: Data-regularized Q, can be combined with any model-free reinforcement learning algorithm. We further demonstrate this by applying it to DQN and significantly improve its data-efficiency on the Atari 100k benchmark.
  • One-sentence Summary: The first successful demonstration that image augmentation can be applied to image-based Deep RL to achieve SOTA performance.
  • Supplementary Material: zip
  • Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
  • Code: [![github](/images/github_icon.svg) denisyarats/drq](
  • Data: [Atari 100k](, [DeepMind Control Suite](
11 Replies