  Deep reinforcement learning (RL) agents often fail to generalize to unseen
scenarios, even when they are trained on many instances of semantically similar
environments. Data augmentation has recently been shown to improve the sample
efficiency and generalization of RL agents. However, different tasks tend to
benefit from different kinds of data augmentation. In this paper, we compare
three approaches for automatically finding an appropriate augmentation. These
are combined with two novel regularization terms for the policy and value
function, required to make the use of data augmentation theoretically sound for
certain actor-critic algorithms. We evaluate our methods on the Procgen
benchmark which consists of 16 procedurally-generated environments and show
that it improves test performance by ~40% relative to standard RL algorithms.
Our agent outperforms other baselines specifically designed to improve
generalization in RL. In addition, we show that our agent learns policies and
representations that are more robust to changes in the environment that do not
affect the agent, such as the background. Our implementation is available at
https://github.com/rraileanu/auto-drac.
