A Survey Of State Representation Learning For Deep Reinforcement Learning

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyechchahed2025a

Related papers

• State Representation Learning For Control: An Overview (2018)17.39
• Unsupervised Representation Learning In Deep Reinforcement Learning: A Review (2022)9.59
• Transfer Learning In Deep Reinforcement Learning: A Survey (2020)20.93
• S-RL Toolbox: Environments, Datasets And Evaluation Metrics For State Representation Learning (2018)0.00
• Bootstrapped Representations In Reinforcement Learning (2023)0.00
• Simsr: Simple Distance-based State Representation For Deep Reinforcement Learning (2021)0.00
• Reinforcement Learning With Knowledge Representation And Reasoning: A Brief Survey (2023)0.00
• Value-consistent Representation Learning For Data-efficient Reinforcement Learning (2022)0.00