“Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels”, Ilya Kostrikov, Denis Yarats, Rob Fergus2020-04-28 (model-free RL; similar)⁠:

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://github.com/denisyarats/drq .

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