Deep reinforcement learning (RL) agents often fail to generalize beyond their training environments. To alleviate this problem, recent work has proposed the use of data augmentation. However, different tasks tend to benefit from different types of augmentations and selecting the right one typically requires expert knowledge. In this paper, we introduce three approaches for automatically finding an effective augmentation for any RL task. 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 actor-critic algorithms. Our method achieves a new state-of-the-art1on the Procgen benchmark and outperforms popular RL algorithms on DeepMind Control tasks with distractors. In addition, our agent learns policies and representations which are more robust to changes in the environment that are irrelevant for solving the task, such as the background.

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We corroborate our theoretical results with numerical experiments.

In this work, we provide a non-asymptotic analysis for two timescale actor-critic methods under non-i.i.d.

NaiveIS estimator involves products of the form π(at | xt)/µ(at | xt) and is infeasible in practice due to high variance. To control the variance, a line of prior work has focused on operator-based estimation to avoid fullIS products, which reduces the estimation procedure into repeated iterations of off-policyevaluation operators [1-3].

Generalization to new environments remains a major challenge in deep reinforcement learning (RL). Current methods fail to generalize to unseen environments even when trained on similar settings [19, 51, 71, 11, 21, 12, 60].

The goal of reinforcement learning (RL) [39] is to maximize the expected total reward by taking actions according toapolicyinastochastic environment, whichismodelled asaMarkovdecision process (MDP) [4]. To obtain an optimal policy, one popular method is the direct maximization of the expected total reward via gradient ascent, which is referred to as the policy gradient (PG) method [40,47].

This is particularly challenging for high-dimensional control tasks, in which there may be a large number of factors that influence the agent's objective.