Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
This addresses generalization challenges in visual RL for robotics and autonomous systems, offering an incremental improvement over existing data augmentation techniques.
The paper tackles the problem of improving generalization in visual reinforcement learning by proposing Task-aware Lipschitz Data Augmentation (TLDA), which selectively augments task-irrelevant pixels to avoid instability, resulting in improved sample efficiency and outperforming state-of-the-art methods across three benchmarks.
One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments. Recently, data augmentation techniques aiming at enhancing data diversity have demonstrated proven performance in improving the generalization ability of learned policies. However, due to the sensitivity of RL training, naively applying data augmentation, which transforms each pixel in a task-agnostic manner, may suffer from instability and damage the sample efficiency, thus further exacerbating the generalization performance. At the heart of this phenomenon is the diverged action distribution and high-variance value estimation in the face of augmented images. To alleviate this issue, we propose Task-aware Lipschitz Data Augmentation (TLDA) for visual RL, which explicitly identifies the task-correlated pixels with large Lipschitz constants, and only augments the task-irrelevant pixels. To verify the effectiveness of TLDA, we conduct extensive experiments on DeepMind Control suite, CARLA and DeepMind Manipulation tasks, showing that TLDA improves both sample efficiency in training time and generalization in test time. It outperforms previous state-of-the-art methods across the 3 different visual control benchmarks.