Automatic Data Augmentation for Generalization in Deep Reinforcement Learning (2006.12862v2)

Abstract: 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.

• The paper presents Data-regularized Actor-Critic (DrAC), which automatically selects data augmentation strategies to improve generalization in reinforcement learning.
• It employs UCB-DrAC, RL2-DrAC, and Meta-DrAC methods along with novel regularization terms to enforce policy and value invariance during training.
• Empirical results on Procgen and DeepMind Control tasks show that DrAC outperforms traditional RL algorithms in adaptability and robustness.

Automatic Data Augmentation for Generalization in Reinforcement Learning

The paper under discussion addresses a significant challenge in deep reinforcement learning (RL): the generalization of RL agents to new environments beyond their training settings. This limitation often results in agents learning to memorize specific trajectories instead of acquiring transferrable skills. The authors propose a novel approach, Data-regularized Actor-Critic (DrAC), which integrates automatic data augmentation strategies into RL to enhance generalization capabilities.

Core Contributions

1. Automatic Augmentation Selection: The paper introduces three methods for automatically selecting an effective data augmentation technique tailored to specific RL tasks, overcoming the limitation of requiring expert knowledge to choose appropriate augmentations manually. This is accomplished through:
   • UCB-DrAC: Utilizes an Upper Confidence Bound-based bandit algorithm to select augmentations from a predetermined set.
   • RL2-DrAC: Employs meta-learning to adaptively choose an augmentation strategy.
   • Meta-DrAC: Meta-learns the parameters of a convolutional network, providing a dynamic augmentation strategy without predefined transformations.
2. Theoretical Grounding with Regularization: To ensure that the application of data augmentation to actor-critic algorithms is theoretically sound, the authors introduce two novel regularization terms. These terms enforce invariance in both the policy and value functions to various state transformations, ensuring consistency and stability in the learning process.
3. Empirical Results: The proposed DrAC method achieves state-of-the-art results on the Procgen benchmark, which includes 16 procedurally generated environments with visual observations. It also surpasses existing RL algorithms on the DeepMind Control tasks with distractors, indicating the robustness of the learned policies and representations to environmental changes.

Implications and Future Directions

Practically, this research offers an effective mechanism to enhance RL agents' adaptability and robustness, especially in environments where observation changes do not affect the task's underlying dynamics. Theoretically, it paves the way for integrating principled data augmentation techniques into various RL frameworks, particularly those with discrete stochastic policies.

The paper lays a foundation for future developments in automatic data augmentation, where more sophisticated and generalized transformation functions could be explored. Additionally, while the paper showcases improvements across a diverse set of tasks, further investigation into task-specific augmentations and their theoretical underpinnings could yield more tailored solutions for complex RL environments.

In conclusion, this work represents a meaningful advancement in RL by mitigating overfitting through automatic augmentation, offering a scalable solution adaptable to vast and varied domains. This contributes not only to enhanced performance in controlled settings but also carries implications for real-world applications where generalization is paramount.