Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels (2004.13649v4)

Abstract: We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The approach leverages input perturbations commonly used in computer vision tasks to regularize the value function. Existing model-free approaches, such as Soft Actor-Critic (SAC), are not able to train deep networks effectively from image pixels. However, the addition of our augmentation method dramatically improves SAC's performance, enabling it to reach state-of-the-art performance on the DeepMind control suite, surpassing model-based (Dreamer, PlaNet, and SLAC) methods and recently proposed contrastive learning (CURL). Our approach can be combined with any model-free reinforcement learning algorithm, requiring only minor modifications. An implementation can be found at https://sites.google.com/view/data-regularized-q.

• The paper presents DrQ as a novel method that applies image augmentation to reduce overfitting in deep reinforcement learning from pixels.
• The approach leverages regularization by averaging Q-values over transformed images to improve stability and sample efficiency.
• DrQ achieves state-of-the-art performance on benchmarks like the DeepMind Control Suite and Atari 100k, emphasizing its broad applicability.

Overview of "Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels"

This paper discusses an innovative data augmentation technique for enhancing the performance and robustness of model-free reinforcement learning (RL) algorithms, particularly when training from pixel inputs. The primary approach, termed "Data-regularized Q" (DrQ), employs image augmentations to address the challenges of sample-efficient learning directly from pixel-based observations. These augmentations help mitigate overfitting and improve the convergence of standard RL methods, such as Soft Actor-Critic (SAC) and Deep Q-Network (DQN), without needing auxiliary losses or pre-training phases.

Core Contributions

The authors highlight several key contributions:

1. Image Augmentation for Reinforcement Learning: The paper introduces straightforward image augmentation strategies that enhance performance significantly over traditional approaches. By applying perturbations, such as random shifts, the input data is transformed in a way that maintains task-relevant information while diversifying the training set.
2. Value Function Regularization: Novel regularization methods leverage Markov Decision Process (MDP) structures to ensure that Q-function learning is invariant to certain state transformations. This is achieved by averaging $Q$-function targets and values over transformed images, leading to reduced variance and improved stability.
3. State-of-the-Art Performance: The approach achieves superior results on benchmarks such as the DeepMind control suite and Atari 100k environments. DrQ demonstrates its effectiveness by outperforming state-of-the-art methods, including both model-free and model-based strategies, such as SAC-AE, SLAC, and Dreamer.
4. Broad Applicability: The technique is compatible with any model-free RL algorithm, simplifying integration and maintaining computational efficiency. DrQ's implementation remains elegant, requiring no complex modifications to the existing algorithms.

Numerical Results

DrQ achieves remarkable performance improvements across several benchmarks:

• DeepMind Control Suite: DrQ outperforms baseline methods in both sample efficiency and asymptotic performance. It matches or surpasses the performance of SAC trained on state information, indicating its effectiveness in utilizing pixel data alone.
• Atari 100k Benchmark: In this challenging sample-constrained environment, DrQ combined with an Efficient DQN setup surpasses other advanced methods like OTRainbow and CURL, highlighting its strength in discrete action spaces.

Theoretical and Practical Implications

This research underscores the significance of data augmentation in reinforcement learning from pixels. The results not only validate the efficacy of input perturbations in tackling overfitting but also pave the way for more accessible and reliable RL deployments in real-world applications, such as robotics and autonomous systems.

From a theoretical standpoint, the proposed regularization methods offer a fresh perspective on leveraging MDP structures for improved Q-function learning. These insights could inform future research directions in reinforcement learning, focusing on efficient representation learning and adaptation.

Future Developments

Moving forward, it's plausible that further exploration could involve:

• Generalizing DrQ to a wider range of environments and task structures.
• Investigating the combination of DrQ with advanced neural architectures or reinforcement learning paradigms.
• Extending the formalism to address challenges in multi-agent or partially observable settings.

In conclusion, this paper contributes a robust and practical approach to enhancing sample-efficient reinforcement learning, reinforcing the role of data augmentation in extracting meaningful insights from pixel data.