Image Augmentation Is All You Need: Regularizing Deep Reinf Learning Fr Pixels(sites.google.com)
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Image Augmentation Is All You Need: Regularizing Deep Reinf Learning Fr Pixels
https://sites.google.com/view/data-regularized-q
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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, SLAC, PlaNet) methods and recently proposed contrastive learning (CURL). Our approach can be combined with any model-free reinforcement learning algorithm, requiring only minor modifications.
- Ilya Kostrikov, Denis Yarats, Rob Fergus