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Automatic Data Augmentation by Upper Confidence Bounds for Deep Reinforcement Learning

In visual reinforcement learning (RL), various approaches succeeded to improve data efficiency. However, the approaches fail to show generalization capabilities if different colors or backgrounds are applied to its environment. The lack of generalization capabilities can hinder the use of RL in real-world environment, which contains lot of distractions and noises. In this paper, a novel automatic data augmentation method that can improve generalization capabilities of an RL agent. In the experiments, the proposed method shows better generalization capabilities than other approaches. These results provide a simple automatic data augmentation method for RL that can improve generalization capabilities.