  Data augmentation is a widely used technique for improving model performance
in machine learning, particularly in computer vision and natural language
processing. Recently, there has been increasing interest in applying
augmentation techniques to reinforcement learning (RL) problems, with a focus
on image-based augmentation. In this paper, we explore a set of generic
wrappers designed to augment RL environments with noise and encourage agent
exploration and improve training data diversity which are applicable to a broad
spectrum of RL algorithms and environments. Specifically, we concentrate on
augmentations concerning states, rewards, and transition dynamics and introduce
two novel augmentation techniques. In addition, we introduce a noise rate
hyperparameter for control over the frequency of noise injection. We present
experimental results on the impact of these wrappers on return using three
popular RL algorithms, Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), and
Proximal Policy Optimization (PPO), across five MuJoCo environments. To support
the choice of augmentation technique in practice, we also present analysis that
explores the performance these techniques across environments. Lastly, we
publish the wrappers in our noisyenv repository for use with gym environments.
