A Survey of State Representation Learning for Deep Reinforcement Learning (2506.17518v1)

Abstract: Representation learning methods are an important tool for addressing the challenges posed by complex observations spaces in sequential decision making problems. Recently, many methods have used a wide variety of types of approaches for learning meaningful state representations in reinforcement learning, allowing better sample efficiency, generalization, and performance. This survey aims to provide a broad categorization of these methods within a model-free online setting, exploring how they tackle the learning of state representations differently. We categorize the methods into six main classes, detailing their mechanisms, benefits, and limitations. Through this taxonomy, our aim is to enhance the understanding of this field and provide a guide for new researchers. We also discuss techniques for assessing the quality of representations, and detail relevant future directions.

State Representation Learning for Deep Reinforcement Learning: A Comprehensive Survey

The survey paper titled "A Survey of State Representation Learning for Deep Reinforcement Learning" by Ayoub Echchahed and Pablo Samuel Castro provides an extensive overview of techniques developed to enhance the efficacy of state representation learning within the context of deep reinforcement learning (DRL). This paper focuses on addressing the fundamental challenges posed by complex observation spaces typical in sequential decision-making problems, such as those encountered in model-free online DRL settings.

Introduction and Motivation

State representation learning (SRL) aims to distill complex sensory inputs into compact, structured representations that prioritize task-relevant information while filtering out irrelevant features. In reinforcement learning (RL), this capability is crucial for addressing the "state-space explosion" associated with high-dimensional observation spaces, which can severely limit the performance of traditional end-to-end RL approaches. The paper acknowledges the rise of methods that decouple representation learning from policy learning to improve sample efficiency, generalization, and robustness in DRL environments.

Taxonomy of Methods

The survey categorizes the diverse SRL techniques into six primary classes:

1. Metric-based Methods: These methods utilize task-relevant metrics to shape the state representation space, capturing behavioral similarities between states. This approach leverages dynamic information like rewards and transitions to improve sample efficiency and generalization. However, computational challenges such as the Wasserstein distance used in bisimulation metrics can pose limitations.
2. Auxiliary Tasks Methods: By introducing additional functions for prediction, these methods enhance state representations. They serve as regularizers and provide extra learning signals, especially in sparse-reward or exploration-intensive settings.
3. Data Augmentation Methods: Techniques under this category apply geometric and photometric transformations to enforce invariance to irrelevant visual changes, simplifying learning processes and enhancing robustness.
4. Contrastive Learning Methods: Through techniques such as InfoNCE loss, these methods align representations by contrasting positive against negative observation pairs, focusing on creating invariance and structured latent spaces.
5. Non-Contrastive Learning Methods: These approaches focus singularly on positive pairs without relying on negative distinctions, thereby avoiding the need for negative sampling while ensuring non-collapse through architectural or regularization techniques.
6. Attention-based Methods: These techniques enhance computational efficiency by focusing on relevant features through attentive mechanisms, improving interpretability and decision-making.

Evaluation and Benchmarking

The paper provides insights into the evaluation aspects pertinent to assessing the quality of state representations. It discusses various metrics and downstream applications that can indirectly evaluate the informativeness and generalization potential of learned representations, using benchmarks based on performance, sample efficiency, generalization, and robustness across different environments.

Future Directions

The survey speculates on several promising directions for SRL in DRL, including multi-task learning, offline pre-training, pre-trained visual representations, zero-shot RL, leveraging LLMs for incorporating prior knowledge, and multi-modal representation learning. Each direction is discussed with potential benefits and challenges, offering fertile ground for future research endeavors.

Conclusion

Echchahed and Castro highlight that while SRL methods have considerably progressed, further exploration and development are necessary to extend their applicability to broader domains, especially as environments grow increasingly complex. This comprehensive survey provides an invaluable resource for researchers seeking to deepen their understanding of current SRL techniques and paves the way for refining these strategies to better address the intricacies in modern RL applications.