# A Survey of State Representation Learning for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.17518 · HTML: https://mlsanity.com/paper/2506.17518/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 120 of its 150 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 30 of 120)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [CURL: Contrastive Unsupervised Representations for Reinforcement Learning](https://mlsanity.com/paper/2004.04136/) (2020)
- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https://mlsanity.com/paper/2004.13649/) (2020)
- [Reinforcement Learning with Augmented Data](https://mlsanity.com/paper/2004.14990/) (2020)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Learning Invariant Representations for Reinforcement Learning without Reconstruction](https://mlsanity.com/paper/2006.10742/) (2020)
- [Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning](https://mlsanity.com/paper/2107.09645/) (2021)
- [Data-Efficient Reinforcement Learning with Self-Predictive Representations](https://mlsanity.com/paper/2007.05929/) (2020)
- [Contextual Decision Processes with Low Bellman Rank are PAC-Learnable](https://mlsanity.com/paper/1610.09512/) (2016)
- [Decoupling Representation Learning from Reinforcement Learning](https://mlsanity.com/paper/2009.08319/) (2020)
- [Masked Visual Pre-training for Motor Control](https://mlsanity.com/paper/2203.06173/) (2022)
- [Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?](https://mlsanity.com/paper/2303.18240/) (2023)
- [Multi-Task Reinforcement Learning with Context-based Representations](https://mlsanity.com/paper/2102.06177/) (2021)
- [Provably efficient RL with Rich Observations via Latent State Decoding](https://mlsanity.com/paper/1901.09018/) (2019)
- [RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank](https://mlsanity.com/paper/2210.02885/) (2022)
- [DARLA: Improving Zero-Shot Transfer in Reinforcement Learning](https://mlsanity.com/paper/1707.08475/) (2017)
- [Unsupervised State Representation Learning in Atari](https://mlsanity.com/paper/1906.08226/) (2019)

## Built on by (most cited first, 17 of 17)

- [Dual Goal Representations](https://mlsanity.com/paper/2510.06714/) (2025)
- [Stable Deep Reinforcement Learning via Isotropic Gaussian Representations](https://mlsanity.com/paper/2602.19373/) (2026)
- [Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning](https://mlsanity.com/paper/2507.14748/) (2025)
- [Deep SPI: Safe Policy Improvement via World Models](https://mlsanity.com/paper/2510.12312/) (2025)
- [PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations](https://mlsanity.com/paper/2512.13093/) (2025)
- [Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics](https://mlsanity.com/paper/2602.21203/) (2026)
- [Abstraction for Offline Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2605.22711/) (2026)
- [Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning](https://mlsanity.com/paper/2606.21943/) (2026)
- [MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning](https://mlsanity.com/paper/2605.09364/) (2026)
- [State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models](https://mlsanity.com/paper/2602.15858/) (2026)
- [Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition](https://mlsanity.com/paper/2603.13904/) (2026)
- [Belief-State RWKV for Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/2604.09671/) (2026)
- [Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning](https://mlsanity.com/paper/2605.26012/) (2026)
- [Representation Learning Enables Scalable Multitask Deep Reinforcement Learning](https://mlsanity.com/paper/2606.05555/) (2026)
- [A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning](https://mlsanity.com/paper/2606.16933/) (2026)
- [Compositional Behavioral Semantics for State Abstraction in Reinforcement Learning](https://mlsanity.com/paper/2606.25357/) (2026)
- [V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control](https://mlsanity.com/paper/2608.07870/) (2026)

## Abstract (arXiv)

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.