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

• URL: http://arxiv.org/abs/2004.13649v4
• Date: Sun, 7 Mar 2021 16:37:37 GMT
• Title: Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
• Authors: Ilya Kostrikov, Denis Yarats, Rob Fergus
• Abstract summary: We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms. We leverage input perturbations commonly used in computer vision tasks to regularize the value function. Our approach can be combined with any model-free reinforcement learning algorithm, requiring only minor modifications.
• Score: 37.726433732939114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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