A Survey of State Representation Learning for Deep Reinforcement Learning

2025-06-20

AI Summary

To address the challenges of learning effective state representations—namely, poor sample efficiency, weak generalization, and difficulty in disentangling semantics—in deep reinforcement learning with complex observation spaces, this paper systematically surveys state-of-the-art model-free online methods. We propose, for the first time, a unified taxonomy comprising six paradigms: self-supervised learning, contrastive learning, causal modeling, information bottleneck, predictive modeling, and disentangled representation learning. Each paradigm is rigorously characterized along six dimensions: objective function, signal source, structural constraints, optimization mechanism, evaluation protocol, and applicable scenarios. The framework explicitly delineates mechanistic principles, strengths, and limitations of existing approaches, and integrates reproducible benchmarks alongside open research challenges. Our analysis significantly enhances representation interpretability, cross-task generalization, and policy robustness. This work establishes the first scalable, theory-grounded analytical guide for state representation learning in deep RL.

Technology Category

Machine Learning: Representation LearningGame Theory and Economic Paradigms: Adversarial LearningSearch and Optimization: Learning to Search

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Address challenges in complex observation spaces for decision making
• Survey diverse state representation learning methods in RL
• Categorize and evaluate representation quality and future directions

Innovation

• Survey categorizes state representation learning methods
• Explores model-free online setting techniques
• Details six classes with mechanisms and benefits