  Recently, data augmentation (DA) has emerged as a method for leveraging
domain knowledge to inexpensively generate additional data in reinforcement
learning (RL) tasks, often yielding substantial improvements in data
efficiency. While prior work has demonstrated the utility of incorporating
augmented data directly into model-free RL updates, it is not well-understood
when a particular DA strategy will improve data efficiency. In this paper, we
seek to identify general aspects of DA responsible for observed learning
improvements. Our study focuses on sparse-reward tasks with dynamics-invariant
data augmentation functions, serving as an initial step towards a more general
understanding of DA and its integration into RL training. Experimentally, we
isolate three relevant aspects of DA: state-action coverage, reward density,
and the number of augmented transitions generated per update (the augmented
replay ratio). From our experiments, we draw two conclusions: (1) increasing
state-action coverage often has a much greater impact on data efficiency than
increasing reward density, and (2) decreasing the augmented replay ratio
substantially improves data efficiency. In fact, certain tasks in our empirical
study are solvable only when the replay ratio is sufficiently low.
