
Image Augmentation Is All You Need: Regularizing Deep Reinf Learning Fr Pixels - overfitted
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

