A Survey of State Representation Learning for Deep Reinforcement Learning
Authors: Ayoub Echchahed, Pablo Samuel Castro
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Collaboration | Ayoub Echchahed EMAIL Mila Québec AI Institute, Université de Montréal Pablo Samuel Castro EMAIL Mila Québec AI Institute, Université de Montréal Google Deep Mind |
| Pseudocode | No | The paper describes various algorithms and methods but does not provide any structured pseudocode or algorithm blocks for its own methodology. |
| Open Source Code | No | The paper is a survey and does not present new methodology for which code would be released. It mentions 'Open Review: https: // openreview. net/ forum? id= g Ok34v UHtz', which is for peer review, not code release. |
| Open Datasets | No | The paper is a survey and does not perform its own experiments using a specific dataset. While it mentions various datasets (e.g., 'ALE benchmark', 'Atari 100k benchmark', 'Image Net', 'CIFAR-10') in the context of discussing methods from other papers, it does not provide access information for a dataset used in its own research. |
| Dataset Splits | No | The paper is a survey and does not conduct experiments, therefore, it does not provide dataset split information. |
| Hardware Specification | No | The paper is a survey and does not describe any specific hardware used for its own research or experiments. |
| Software Dependencies | No | The paper is a survey and does not describe specific software dependencies with version numbers for its own research or experiments. |
| Experiment Setup | No | The paper is a survey and does not describe its own experimental setup, hyperparameters, or training configurations. |