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Paper Citation Record · LEDGER

A Survey of State Representation Learning for Deep Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 100 of 157 outbound references and 6 inbound Pith citation observations for arXiv:2506.17518.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.17518 v1

Coverage vector

measured 100 of 157 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:34:41.099059Z

measured 106 of 106 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:46:26.662381Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T07:59:40.694649Z

Reference resolution

100 of 157 outbound references displayed

  • verified exact11
  • verified fuzzy0
  • unresolved89
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation fc0284af-a214-4ae6-bd9a-17278f658440 · outbound

This paper cites A Theory of Abstraction in Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning A Theory of Abstraction in Reinforcement Learning

Reference 1

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Observation 7fbfffcb-2458-40b7-a767-c8b2f8e4502d · outbound

This paper cites Machado, Pablo Samuel Castro, and Marc G Bellemare.

A Survey of State Representation Learning for Deep Reinforcement Learning Machado, Pablo Samuel Castro, and Marc G Bellemare

Reference 2

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Observation 70c79a2a-a6f7-400c-8d9c-9cac30786a7d · outbound

This paper cites Bellemare.

A Survey of State Representation Learning for Deep Reinforcement Learning Bellemare

Reference 3

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Observation 683c03af-f7ca-44e0-96ef-bbf2d68e1bd0 · outbound

This paper cites Proto Successor Measure: Representing the Behavior Space of an RL Agent.

A Survey of State Representation Learning for Deep Reinforcement Learning Proto Successor Measure: Representing the Behavior Space of an RL Agent

Reference 4

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Observation 9bebbd1f-1e5a-4262-86dd-9ec8928f9ab0 · outbound

This paper cites Alemi, Ian Fischer, Joshua V.

A Survey of State Representation Learning for Deep Reinforcement Learning Alemi, Ian Fischer, Joshua V

Reference 5

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Observation adedad9f-34c0-4fde-b579-135587ad0138 · outbound

This paper cites Learning markov state abstractions for deep reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Learning markov state abstractions for deep reinforcement learning

Reference 6

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Observation b3b1068e-a4d9-487b-bee4-7c892d5c1c0f · outbound

This paper cites A recipe for unbounded data augmentation in visual reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning A recipe for unbounded data augmentation in visual reinforcement learning

Reference 7

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Observation ffa5f726-b3d2-4455-a861-9d7d46f2316e · outbound

This paper cites Unsupervised State Representation Learning in Atari.

A Survey of State Representation Learning for Deep Reinforcement Learning Unsupervised State Representation Learning in Atari

Reference 8

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Observation 1fb18abd-062d-4b92-a5b8-2b513c57520f · outbound

This paper cites Hindsight experience replay.

A Survey of State Representation Learning for Deep Reinforcement Learning Hindsight experience replay

Reference 9

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Observation 40eb1f6f-2920-43d9-a758-ecf0e3cb527f · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

A Survey of State Representation Learning for Deep Reinforcement Learning Self-supervised learning from images with a joint-embedding predictive architecture

Reference 10

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Observation 9f2277d9-3556-4ae1-904b-9a1fe1c9f621 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

A Survey of State Representation Learning for Deep Reinforcement Learning Neural Machine Translation by Jointly Learning to Align and Translate

Reference 11

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Observation b52123de-b84a-4f66-bcd7-9f470184f823 · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 12

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Observation c6e9439b-652b-4a2f-9c31-bc0b655ce9d1 · outbound

This paper cites Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL.

A Survey of State Representation Learning for Deep Reinforcement Learning Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL

Reference 13

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Observation e00709d0-3b7e-4178-bb9a-6bc6f9229e38 · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.

A Survey of State Representation Learning for Deep Reinforcement Learning The arcade learning environment: An evaluation platform for general agents

Reference 14

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Observation 227c9b5b-29aa-4cf5-ba90-48338c9f003b · outbound

This paper cites Representation Learning: A Review and New Perspectives.

A Survey of State Representation Learning for Deep Reinforcement Learning Representation Learning: A Review and New Perspectives

Reference 15

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Observation 13381cb8-049f-4314-8761-03712ff125be · outbound

This paper cites Look where you look! saliency-guided q-networks for generalization in visual reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Look where you look! saliency-guided q-networks for generalization in visual reinforcement learning

Reference 16

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Observation 6a57f70d-d1ec-4662-ab92-de6fed824494 · outbound

This paper cites Riedmiller, and Klaus Obermayer.

A Survey of State Representation Learning for Deep Reinforcement Learning Riedmiller, and Klaus Obermayer

Reference 17

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Observation 2619fb39-ead0-4c0b-8f64-469fe06d1997 · outbound

This paper cites Unsupervised Representation Learning in Deep Reinforcement Learning: A Review.

A Survey of State Representation Learning for Deep Reinforcement Learning Unsupervised Representation Learning in Deep Reinforcement Learning: A Review

Reference 18

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Observation 56feb069-faba-436e-8b84-adafa5f7a471 · outbound

This paper cites Barlowrl: Barlow twins for data-efficient reinforcement learning, 2023.

A Survey of State Representation Learning for Deep Reinforcement Learning Barlowrl: Barlow twins for data-efficient reinforcement learning, 2023

Reference 19

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Observation 2c7a5e28-6427-413e-8fa0-4eecf71701f8 · outbound

This paper cites Scalable methods for computing state similarity in deterministic markov decision processes.

A Survey of State Representation Learning for Deep Reinforcement Learning Scalable methods for computing state similarity in deterministic markov decision processes

Reference 20

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Observation ad3d8022-57be-4282-8bb6-a97501533644 · outbound

This paper cites MIC o: Improved representations via sampling-based state similarity for markov decision processes.

A Survey of State Representation Learning for Deep Reinforcement Learning MIC o: Improved representations via sampling-based state similarity for markov decision processes

Reference 21

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Observation 0e012f29-b1e3-4c92-8aa2-ab8664145258 · outbound

This paper cites Learning action representations for reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Learning action representations for reinforcement learning

Reference 22

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Observation 92d5298a-6545-475a-8464-58e8249438eb · outbound

This paper cites Why do we need large batchsizes in contrastive learning? a gradient-bias perspective.

A Survey of State Representation Learning for Deep Reinforcement Learning Why do we need large batchsizes in contrastive learning? a gradient-bias perspective

Reference 23

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Observation e48246f5-0fa4-4c5d-97f8-e4df61955af3 · outbound

This paper cites Focus-then-decide: Segmentation-assisted reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Focus-then-decide: Segmentation-assisted reinforcement learning

Reference 24

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Observation dade7f15-f3f4-494a-ab8b-70f658e21596 · outbound

This paper cites Learning representations via a robust behavioral metric for deep reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Learning representations via a robust behavioral metric for deep reinforcement learning

Reference 25

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Observation e44909d6-4cad-49b3-b706-f5a7fc74aa5e · outbound

This paper cites State chrono representation for enhancing generalization in reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning State chrono representation for enhancing generalization in reinforcement learning

Reference 26

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Observation 4aa79829-9bce-4be4-b816-064769238ad5 · outbound

This paper cites Vision-Language Models Provide Promptable Representations for Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Vision-Language Models Provide Promptable Representations for Reinforcement Learning

Reference 27

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Observation 1974af94-c7bb-43b1-9a62-fe563ec95f7c · outbound

This paper cites Exploring simple siamese representation learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Exploring simple siamese representation learning

Reference 28

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Observation 248f4eb1-ed0f-4dd6-b78a-e9817ccd050c · outbound

This paper cites Mudalige, Katharina Muelling, and John M.

A Survey of State Representation Learning for Deep Reinforcement Learning Mudalige, Katharina Muelling, and John M

Reference 29

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Observation 1be9d304-2034-4e27-b33d-6304a32383e7 · outbound

This paper cites Provable benefit of multitask representation learning in reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Provable benefit of multitask representation learning in reinforcement learning

Reference 30

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Observation a3185252-2ef7-4eca-a1e5-0787cbf31e40 · outbound

This paper cites Improving generalisation for temporal difference learning: The successor representation.

A Survey of State Representation Learning for Deep Reinforcement Learning Improving generalisation for temporal difference learning: The successor representation

Reference 31

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source=arxiv_source observed=2026-08-06T23:34:33.966441Z digest=sha256:225ac7eed268fee7aca69798ee894593746ec27206103d06ac0ab75c702c0b02

Observation 249a0d35-e33f-40b8-b04b-c598d219ce36 · outbound

This paper cites Integrating state representation learning into deep reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Integrating state representation learning into deep reinforcement learning

Reference 32

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Observation 247d2adb-97eb-4705-a285-04f5296570b4 · outbound

This paper cites The Hidden Pitfalls of the Cosine Similarity Loss.

A Survey of State Representation Learning for Deep Reinforcement Learning The Hidden Pitfalls of the Cosine Similarity Loss

Reference 33

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Observation 0ea8e572-ee52-4b48-b4ea-934b237d19fc · outbound

This paper cites Provably efficient rl with rich observations via latent state decoding.

A Survey of State Representation Learning for Deep Reinforcement Learning Provably efficient rl with rich observations via latent state decoding

Reference 34

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source=arxiv_source observed=2026-08-06T23:34:34.296516Z digest=sha256:39541525f9e267ec25614bb3c23264d5f7aad0b40466a865ec01acd0ff7a10f7

Observation 52d1e052-1648-407e-a701-f30c4224a965 · outbound

This paper cites Adapting Auxiliary Losses Using Gradient Similarity.

A Survey of State Representation Learning for Deep Reinforcement Learning Adapting Auxiliary Losses Using Gradient Similarity

Reference 35

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source=arxiv_source observed=2026-08-06T23:34:34.373838Z digest=sha256:7e9627e579334c7b32818f79adbb294fa510212888a738ab1fcba5062fe84707

Observation 5e7ef77a-933e-402d-b3a3-f772ddcd6f7b · outbound

This paper cites Multi-view disentanglement for reinforcement learning with multiple cameras.

A Survey of State Representation Learning for Deep Reinforcement Learning Multi-view disentanglement for reinforcement learning with multiple cameras

Reference 36

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source=arxiv_source observed=2026-08-06T23:34:34.475817Z digest=sha256:f818fc79d4d668492f30a46b291fc6e1afb7849b78b81f796fa010e97244d9b7

Observation 5f7a9eeb-dcbe-4932-94c2-e03269cefae5 · outbound

This paper cites Hanna, and Stefano V Albrecht.

A Survey of State Representation Learning for Deep Reinforcement Learning Hanna, and Stefano V Albrecht

Reference 37

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source=arxiv_source observed=2026-08-06T23:34:34.560683Z digest=sha256:a2b6db7252ec33b30bac4d63fc9ae9faa237c0fd5338375d5612fa96c0f58f02

Observation 0aafa79e-c019-4a88-bc8d-f0c2c87e6696 · outbound

This paper cites Conditional mutual information for disentangled representations in reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Conditional mutual information for disentangled representations in reinforcement learning

Reference 38

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source=arxiv_source observed=2026-08-06T23:34:34.640892Z digest=sha256:fd4f5db0ba6be7431c35c9483c32d06c448c9c79ef222a66782e4710404e6310

Observation 769176f0-4365-4a03-aa81-988eafb6751e · outbound

This paper cites Provable benefits of representational transfer in reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Provable benefits of representational transfer in reinforcement learning

Reference 39

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Observation 7dae1c0f-864c-4e9e-99d9-c9d633a39f6c · outbound

This paper cites Dribo: Robust deep reinforcement learning via multi-view information bottleneck.

A Survey of State Representation Learning for Deep Reinforcement Learning Dribo: Robust deep reinforcement learning via multi-view information bottleneck

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Observation ec63ff41-2aec-4ab2-97a1-0e9ff0cbbfad · outbound

This paper cites Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks.

A Survey of State Representation Learning for Deep Reinforcement Learning Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks

Reference 41

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Observation 52ea7c01-1655-40b0-88f1-910a0bd89cf5 · outbound

This paper cites Hyperbolic Discounting and Learning over Multiple Horizons.

A Survey of State Representation Learning for Deep Reinforcement Learning Hyperbolic Discounting and Learning over Multiple Horizons

Reference 42

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Observation 0d3781db-7163-44f7-93ae-c881c3318f8a · outbound

This paper cites Metrics for Finite Markov Decision Processes.

A Survey of State Representation Learning for Deep Reinforcement Learning Metrics for Finite Markov Decision Processes

Reference 43

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Observation 247920c0-a5e6-420c-a29b-e68af30476d1 · outbound

This paper cites Self-supervised Learning of Image Embedding for Continuous Control.

A Survey of State Representation Learning for Deep Reinforcement Learning Self-supervised Learning of Image Embedding for Continuous Control

Reference 44

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Observation 0245ae6f-8232-4548-b34a-61184b23c1d9 · outbound

This paper cites For sale: State-action representation learning for deep reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning For sale: State-action representation learning for deep reinforcement learning

Reference 45

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source=arxiv_source observed=2026-08-06T23:34:35.531471Z digest=sha256:ecabd9cc8438df7958934db6b5c697ab53210664db5c68e7c0f75ed25688c395

Observation d4b0f8b5-cc20-45b9-bed2-1406b5bfb2b8 · outbound

This paper cites Towards General-Purpose Model-Free Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Towards General-Purpose Model-Free Reinforcement Learning

Reference 46

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source=arxiv_source observed=2026-08-06T23:34:35.620176Z digest=sha256:b7eb629ef9e46ea5e1305ee26a2654776d2909a7b06c6d9c28ff6b707bad387a

Observation 3d2ecb1d-08cd-4a12-a7b2-92a45a182603 · outbound

This paper cites Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank.

A Survey of State Representation Learning for Deep Reinforcement Learning Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank

Reference 47

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Observation fe9b8ed9-e92e-44c5-8d57-8c22feb906e1 · outbound

This paper cites Learning and Leveraging World Models in Visual Representation Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Learning and Leveraging World Models in Visual Representation Learning

Reference 48

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Observation df0d1b2d-0bce-4672-9c03-a6f1cb2fa667 · outbound

This paper cites an unresolved cited work.

A Survey of State Representation Learning for Deep Reinforcement Learning Unresolved cited work

Reference 49

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source=arxiv_source observed=2026-08-06T23:34:35.796368Z digest=sha256:1adcb975fd3962ec9ae192217c92888ac59b55ff4b287e23a94ffd0bdf2bbe52

Observation ae35fd39-5e53-4b9e-b7ab-5588b39982c1 · outbound

This paper cites Visualizing and understanding atari agents.

A Survey of State Representation Learning for Deep Reinforcement Learning Visualizing and understanding atari agents

Reference 50

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source=arxiv_source observed=2026-08-06T23:34:35.916056Z digest=sha256:1706e232e196d2445bc7f0398445252ce661e66cd38b9e9a966d2595ac7639d6

Observation eca950bb-7425-4834-9125-57c302468bf8 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Bootstrap your own latent-a new approach to self-supervised learning

Reference 51

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source=arxiv_source observed=2026-08-06T23:34:36.001995Z digest=sha256:880ef1dd6f44328bdf25c9b769c2d714e7534771e1ecd7fcf56f603075f8b6f5

Observation fe79a99b-7be5-4b09-a73b-94efda088bd3 · outbound

This paper cites Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Reference 52

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source=arxiv_source observed=2026-08-06T23:34:36.114222Z digest=sha256:b3996942b8b312f6f3f8f2f7ff2ccc3e47d594716cf37b7502eda652f72fd537

Observation 1bba53d9-760b-481c-b512-251f2567ba96 · outbound

This paper cites Stabilizing deep q-learning with convnets and vision transformers under data augmentation.

A Survey of State Representation Learning for Deep Reinforcement Learning Stabilizing deep q-learning with convnets and vision transformers under data augmentation

Reference 53

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source=arxiv_source observed=2026-08-06T23:34:36.221641Z digest=sha256:23e49327ef3012f4d3c08cc5ee782e0c4d771a5206622cd9b8436dfd66952677

Observation e4c8e298-9480-419b-8506-7eced093effa · outbound

This paper cites Masked autoencoders are scalable vision learners.

A Survey of State Representation Learning for Deep Reinforcement Learning Masked autoencoders are scalable vision learners

Reference 54

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source=arxiv_source observed=2026-08-06T23:34:36.304016Z digest=sha256:db6a1e92e42b65f1500458d9cf2592c2904714820a514f7d0dc455d0575a53fe

Observation 3319eecb-fbf2-4385-a9d8-20ad59358d79 · outbound

This paper cites Rainbow: Combining improvements in deep reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Rainbow: Combining improvements in deep reinforcement learning

Reference 55

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source=arxiv_source observed=2026-08-06T23:34:36.386222Z digest=sha256:f6deef23030a5bd42cb4fc3f7a8ed873ec869b62639a2203b1211e3b0b6d1661

Observation e42fb4a2-6188-4938-8fb3-35b3692031e4 · outbound

This paper cites Multi-task deep reinforcement learning with popart.

A Survey of State Representation Learning for Deep Reinforcement Learning Multi-task deep reinforcement learning with popart

Reference 56

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source=arxiv_source observed=2026-08-06T23:34:36.466449Z digest=sha256:c6ca3c7928e612e15ea3066da6ae75eb47cd9ba8cd1c91ec764547bc786ec986

Observation d658735c-0c9c-4693-9503-2df3f5d78b0f · outbound

This paper cites Burgess, Xavier Glorot, Matthew M.

A Survey of State Representation Learning for Deep Reinforcement Learning Burgess, Xavier Glorot, Matthew M

Reference 57

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source=arxiv_source observed=2026-08-06T23:34:36.568115Z digest=sha256:a3b61dea24feef7c886911c11a6db8cb193bff60158fb752bd84b9c680e44b48

Observation 20063910-070a-40ae-8551-acb7357e343d · outbound

This paper cites Darla: Improving zero-shot transfer in reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Darla: Improving zero-shot transfer in reinforcement learning

Reference 58

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source=arxiv_source observed=2026-08-06T23:34:36.642169Z digest=sha256:c7e8e4137d05329e2fbd840fdd5e0aff2c82c0d9b0edd4df4e927c2f21e14b2c

Observation e0e82cff-cc47-44db-942c-9e862567aa81 · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

A Survey of State Representation Learning for Deep Reinforcement Learning Learning deep representations by mutual information estimation and maximization

Reference 59

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source=arxiv_source observed=2026-08-06T23:34:36.711613Z digest=sha256:b08048976f096695ec8d3ba143f2b3900cd3bec7127b3f8dfdf162fbb480ee92

Observation 8bec8ca3-b945-41f2-90d3-e3da9bf3c7ce · outbound

This paper cites Revisiting data augmentation in deep reinforcement learning, 2024.

A Survey of State Representation Learning for Deep Reinforcement Learning Revisiting data augmentation in deep reinforcement learning, 2024

Reference 60

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source=arxiv_source observed=2026-08-06T23:34:36.855098Z digest=sha256:aa4f745af70dc09a80b2295b70ad2a6567e101f200b71dec421423d5ceb209cb

Observation 1eda50b0-e177-42c7-a462-77a8cbae59a0 · outbound

This paper cites Spectrum random masking for generalization in image-based reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Spectrum random masking for generalization in image-based reinforcement learning

Reference 61

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source=arxiv_source observed=2026-08-06T23:34:37.022592Z digest=sha256:4bcd721a0904704cffe2f44a792103b74dd3c8e1aae91d6486e371e35ae017cf

Observation c01cccb3-b34b-4609-8163-e87fd82749fd · outbound

This paper cites Generalization in reinforcement learning with selective noise injection and information bottleneck.

A Survey of State Representation Learning for Deep Reinforcement Learning Generalization in reinforcement learning with selective noise injection and information bottleneck

Reference 62

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source=arxiv_source observed=2026-08-06T23:34:37.261134Z digest=sha256:ead1adfc68743504de8334bdf2a09a79ab4f3341e6ccad9ebf7d03f0a3095027

Observation 0433bb9d-31b3-4215-9bcc-8f11a457e792 · outbound

This paper cites Zero-shot reinforcement learning via function encoders.

A Survey of State Representation Learning for Deep Reinforcement Learning Zero-shot reinforcement learning via function encoders

Reference 63

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source=arxiv_source observed=2026-08-06T23:34:37.449983Z digest=sha256:0eca798ee9397f1e6567f94092b0028a56f59b0a5416ff2b15f455aa4b094d99

Observation 0d12324e-724e-4463-a270-a5083354bba0 · outbound

This paper cites Offline multitask representation learning for reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Offline multitask representation learning for reinforcement learning

Reference 64

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source=arxiv_source observed=2026-08-06T23:34:37.588177Z digest=sha256:15d46142be724c1ad5be8d52ddbe316862f5cbcefb9316bcd5716d8b026f4d39

Observation 7be4c4ed-aeee-4cbe-8eda-9393ce835ad4 · outbound

This paper cites Principled offline rl in the presence of rich exogenous information.

A Survey of State Representation Learning for Deep Reinforcement Learning Principled offline rl in the presence of rich exogenous information

Reference 65

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source=arxiv_source observed=2026-08-06T23:34:37.663016Z digest=sha256:0a70da29df5324fb0caac67ca7e652cc090850c99151c0834eaa21c120dbca6a

Observation 65ac7c63-354d-422b-a49e-774ee331824f · outbound

This paper cites Representation learning in deep rl via discrete information bottleneck.

A Survey of State Representation Learning for Deep Reinforcement Learning Representation learning in deep rl via discrete information bottleneck

Reference 66

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source=arxiv_source observed=2026-08-06T23:34:37.743091Z digest=sha256:78435f2b7cc14d064a8387bbbff48d892232de65caadba2985e53800feb443f7

Observation 9c91ce1a-d6fc-4b13-a839-2584aca7db98 · outbound

This paper cites Zero-shot reinforcement learning from low quality data.

A Survey of State Representation Learning for Deep Reinforcement Learning Zero-shot reinforcement learning from low quality data

Reference 67

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source=arxiv_source observed=2026-08-06T23:34:37.839439Z digest=sha256:9de92d2f0a2a02025eb4e6afb92524a877b552b3fbdeb1ae8e927b3935bc96db

Observation b709a84a-fdaf-452f-a4d8-14eb2231df6f · outbound

This paper cites Contextual decision processes with low bellman rank are pac-learnable.

A Survey of State Representation Learning for Deep Reinforcement Learning Contextual decision processes with low bellman rank are pac-learnable

Reference 68

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source=arxiv_source observed=2026-08-06T23:34:37.956454Z digest=sha256:3168e53eadce56c55591f65fae39d82f7d0a889fba6c9ad3377332bdff794bd2

Observation 7d7f1d07-9b23-4454-b864-4851c395dc9d · outbound

This paper cites Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning

Reference 69

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source=arxiv_source observed=2026-08-06T23:34:38.133164Z digest=sha256:d759c5710fe49938bd744079e1947bbd903f93025baf7b3386f7ca970b6d3603

Observation e1d3cdb0-89c9-420a-b031-d3b7b0f09b42 · outbound

This paper cites PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations.

A Survey of State Representation Learning for Deep Reinforcement Learning PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations

Reference 70

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source=arxiv_source observed=2026-08-06T23:34:38.254561Z digest=sha256:260fe62bf941f3809403123dbc3be68a767e66d4d0041619ae940d6baa73d177

Observation 31800c70-4ce7-4a0a-9ece-0ff30c243311 · outbound

This paper cites an unresolved cited work.

A Survey of State Representation Learning for Deep Reinforcement Learning Unresolved cited work

Reference 71

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source=arxiv_source observed=2026-08-06T23:34:38.443902Z digest=sha256:3b123660d3d63a11d76984b167ffba4e91eb9f59e3297dc5bbc5918f87a1f43f

Observation c85309c9-25de-4d18-b020-ccf4c33d33cc · outbound

This paper cites Scaling up multi-task robotic reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Scaling up multi-task robotic reinforcement learning

Reference 72

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source=arxiv_source observed=2026-08-06T23:34:38.671178Z digest=sha256:45eb4a6e5bafd4d70d88177da8afca97fbbd1378623afba8ce199ae9fe937dea

Observation a710befc-6a13-4041-b862-654ee240e9bd · outbound

This paper cites Terminal prediction as an auxiliary task for deep reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Terminal prediction as an auxiliary task for deep reinforcement learning

Reference 73

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source=arxiv_source observed=2026-08-06T23:34:38.806508Z digest=sha256:e78755b87fa7ab15ae814f4737f2af7e31cdee4c9079ce10fc7e311a202961ce

Observation 5f860681-6a52-44bd-8162-3a08283fc4dc · outbound

This paper cites Towards Robust Bisimulation Metric Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Towards Robust Bisimulation Metric Learning

Reference 74

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source=arxiv_source observed=2026-08-06T23:34:38.893179Z digest=sha256:908571c5905166073bef3c88726d42e5b3b2d40ca0f3f209b939ae8dd0b3d51e

Observation f0c82f2f-9e5e-49b2-833d-cb51c9ae672b · outbound

This paper cites A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning

Reference 75

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source=arxiv_source observed=2026-08-06T23:34:39.068965Z digest=sha256:990a1243b165369275387e15abff5e2850c0447afb1c5ef81a2355d1be02a74e

Observation 744b1512-e3c0-45ec-9d1e-50d5ebdc8aab · outbound

This paper cites Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning

Reference 76

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source=arxiv_source observed=2026-08-06T23:34:39.159134Z digest=sha256:78f6dd0efc2634573d16fe1d0e941b62ba9a84e5a4d7467ab429f70683d838aa

Observation 0dbccc72-5fb9-4044-918e-4a195eee8286 · outbound

This paper cites Auto-Encoding Variational Bayes.

A Survey of State Representation Learning for Deep Reinforcement Learning Auto-Encoding Variational Bayes

Reference 77

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source=arxiv_source observed=2026-08-06T23:34:39.165844Z digest=sha256:8c794b1e36b8fe8e69a65553ec89c66db0c7cff4e05a5d123d8febfcb20a6f9b

Observation 8fe5094d-cd03-427a-a807-38d940521959 · outbound

This paper cites Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels.

A Survey of State Representation Learning for Deep Reinforcement Learning Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

Reference 78

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source=arxiv_source observed=2026-08-06T23:34:39.216865Z digest=sha256:4faafdc154957c8587e5e3d6fd5946def6a2f353e3d02b131cf332be0d867471

Observation 89f31156-3149-4ae3-baab-e01d2f9308ec · outbound

This paper cites Pac reinforcement learning with rich observations.

A Survey of State Representation Learning for Deep Reinforcement Learning Pac reinforcement learning with rich observations

Reference 79

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source=arxiv_source observed=2026-08-06T23:34:39.274360Z digest=sha256:4d4098e47c235a8aa4202aabf108bce6f9c9e57e390801b03a1f7bb0c6e11f00

Observation 6bf1343d-dd05-4ffd-85eb-2a06c9066dc6 · outbound

This paper cites Guaranteed discovery of control-endogenous latent states with multi-step inverse models.

A Survey of State Representation Learning for Deep Reinforcement Learning Guaranteed discovery of control-endogenous latent states with multi-step inverse models

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source=arxiv_source observed=2026-08-06T23:34:39.320161Z digest=sha256:921bc12bd0e8361aeaa8381718c7bb64301c0b7956334702656151ef9faf781c

Observation 4f218946-ab8e-4744-b4c3-a7b34b8e74de · outbound

This paper cites Reinforcement Learning with Augmented Data.

A Survey of State Representation Learning for Deep Reinforcement Learning Reinforcement Learning with Augmented Data

Reference 81

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source=arxiv_source observed=2026-08-06T23:34:39.383806Z digest=sha256:da9d4bbe5167a891099c7d42dd4b99766c103a6b4089ff32146493839b3259be

Observation a2fa7013-cd29-4408-868a-d9aa59bf8b5d · outbound

This paper cites Metrics and continuity in reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Metrics and continuity in reinforcement learning

Reference 82

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source=arxiv_source observed=2026-08-06T23:34:39.433199Z digest=sha256:4c0e6c4bd70a08cbafe62399cdabd724b93dadfe3271f3786d0aabad35ad597f

Observation 90edb0d9-672e-4a28-8ef6-c372ec1c25e6 · outbound

This paper cites A path towards autonomous machine intelligence version 0.9.

A Survey of State Representation Learning for Deep Reinforcement Learning A path towards autonomous machine intelligence version 0.9

Reference 83

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source=arxiv_source observed=2026-08-06T23:34:39.490486Z digest=sha256:52f1b30992fd26842a6976ab2d7501e2595b5574d9523ffe34b5c9d6fc3945be

Observation fb10e4d9-1a12-48df-8e88-fa51638cb088 · outbound

This paper cites Unsupervised state representation learning with robotic priors: a robustness benchmark.

A Survey of State Representation Learning for Deep Reinforcement Learning Unsupervised state representation learning with robotic priors: a robustness benchmark

Reference 85

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source=arxiv_source observed=2026-08-06T23:34:39.629835Z digest=sha256:a7a5b916cd72e6dd9905a9f0c8b39da6f5b7a69d7c363750b8b12c8ff34f54e1

Observation 453d13b6-58a2-45eb-89f8-95f7d3a24ff5 · outbound

This paper cites State Representation Learning for Control: An Overview.

A Survey of State Representation Learning for Deep Reinforcement Learning State Representation Learning for Control: An Overview

Reference 86

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source=arxiv_source observed=2026-08-06T23:34:39.756981Z digest=sha256:34f66e3bcaed3f20b1a3e5d052985d6a5b85008fffb11b5cfdc0c331f3b7c2f4

Observation e7d23b61-9307-4e5b-be45-8bcdc6ae0445 · outbound

This paper cites Normalization enhances generalization in visual reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Normalization enhances generalization in visual reinforcement learning

Reference 87

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source=arxiv_source observed=2026-08-06T23:34:39.836538Z digest=sha256:18538ec1f57b4bf17bd08a21911a84ce80cbe8e44fada89ea3c99f066a11df05

Observation c02c11b6-0b44-485c-ad2f-cbf3405d2689 · outbound

This paper cites Provable general function class representation learning in multitask bandits and mdp.

A Survey of State Representation Learning for Deep Reinforcement Learning Provable general function class representation learning in multitask bandits and mdp

Reference 88

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source=arxiv_source observed=2026-08-06T23:34:39.891192Z digest=sha256:e94a90f6da36770fdb246377ba5f961374c6af96dfea3a4187fa342cf8951427

Observation e7eb5e7d-1038-4773-8ba9-97e034c0e064 · outbound

This paper cites On The Effect of Auxiliary Tasks on Representation Dynamics.

A Survey of State Representation Learning for Deep Reinforcement Learning On The Effect of Auxiliary Tasks on Representation Dynamics

Reference 89

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source=arxiv_source observed=2026-08-06T23:34:39.980915Z digest=sha256:4265fb348312c93146bc75fc03fb77fb22869b880b58d17cb3b9fd0a3ee6da75

Observation 51bcdc9a-5394-4412-932f-b6e6ec3ea9ff · outbound

This paper cites A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning

Reference 90

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source=arxiv_source observed=2026-08-06T23:34:40.091630Z digest=sha256:4da0ddb8f3da299c2f7e5b750801db5807f7edb6c8fa99ad78bc0cb2394d20e3

Observation a91ebd6d-25b4-4462-894d-84d4a62889e3 · outbound

This paper cites Revisiting plasticity in visual reinforcement learning: Data, modules and training stages.

A Survey of State Representation Learning for Deep Reinforcement Learning Revisiting plasticity in visual reinforcement learning: Data, modules and training stages

Reference 91

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source=arxiv_source observed=2026-08-06T23:34:40.183919Z digest=sha256:bb432497fc1f95292eaa66008bf74c94c1eb94e62a7d67d2996de6d201701da0

Observation 567422ef-5732-429e-abac-83880cf997ca · outbound

This paper cites VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training.

A Survey of State Representation Learning for Deep Reinforcement Learning VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training

Reference 92

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source=arxiv_source observed=2026-08-06T23:34:40.275191Z digest=sha256:1c0b6ac6a0e5340842f411780e637384cc4f24dc3dba7eeeb810945c6e8cec78

Observation 8fb15b68-72f8-48fa-9d00-88093d78969a · outbound

This paper cites Where are we in the search for an artificial visual cortex for embodied intelligence? Advances in Neural Information Processing Systems, 36: 0 655--677, 2023.

A Survey of State Representation Learning for Deep Reinforcement Learning Where are we in the search for an artificial visual cortex for embodied intelligence? Advances in Neural Information Processing Systems, 36: 0 655--677, 2023

Reference 93

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source=arxiv_source observed=2026-08-06T23:34:40.386436Z digest=sha256:2c2d9622becb0f7048b09374ba4c4ba55abafc9e0978d424cb049b6bff144253

Observation 7fb0d396-eee1-45ca-8b00-4bfa7703e8e4 · outbound

This paper cites Deep reinforcement and infomax learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Deep reinforcement and infomax learning

Reference 94

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source=arxiv_source observed=2026-08-06T23:34:40.542795Z digest=sha256:633f73317b032b7dee8bdcdea1fd1e2967ef57c7838466ae0b45009aea466c8e

Observation 934a1bf8-71fe-4c20-8892-6045ae85f9c7 · outbound

This paper cites Multi-horizon representations with hierarchical forward models for reinforcement learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Multi-horizon representations with hierarchical forward models for reinforcement learning

Reference 95

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source=arxiv_source observed=2026-08-06T23:34:40.720190Z digest=sha256:b749e170d765a2be58470a04d5404443526e794b7e56938c086f558deba152da

Observation 248ddb6e-8d6f-42f7-9245-1ea4c28fb5c0 · outbound

This paper cites Towards Principled Representation Learning from Videos for Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Towards Principled Representation Learning from Videos for Reinforcement Learning

Reference 96

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source=arxiv_source observed=2026-08-06T23:34:40.760414Z digest=sha256:2c0677e53c709f6e59a2055e0aaa460f54634b5055aab94cae0511df2f584c9a

Observation 0f6b23f9-3eb1-4c57-9a74-015de97bc54f · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

A Survey of State Representation Learning for Deep Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 97

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source=arxiv_source observed=2026-08-06T23:34:40.865066Z digest=sha256:6edb6f44a309a75501d91a4af9bab3fd326d806509d57eeb3194086ea9fd4786

Observation c766b3fe-7979-4301-9978-5fffa20c34fb · outbound

This paper cites Towards Interpretable Reinforcement Learning Using Attention Augmented Agents.

A Survey of State Representation Learning for Deep Reinforcement Learning Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Reference 98

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source=arxiv_source observed=2026-08-06T23:34:40.942699Z digest=sha256:b1315c5e35d9b60ea3b4b151f68c890aae15a3c5ca4b7d033c3e28cd04077550

Observation ea189003-f8f4-4171-ab06-f01a02fe996c · outbound

This paper cites R3m: A universal visual representation for robot manipulation, 2022.

A Survey of State Representation Learning for Deep Reinforcement Learning R3m: A universal visual representation for robot manipulation, 2022

Reference 99

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source=arxiv_source observed=2026-08-06T23:34:41.037102Z digest=sha256:53b65a5cd891eebb2fcf65bbff14da7558c57f08b406762c2d882e48b74ab7a7

Observation ca1dcd19-da73-42e5-960c-7d4f0b9a39e0 · outbound

This paper cites Bridging state and history representations: Understanding self-predictive rl, 2024.

A Survey of State Representation Learning for Deep Reinforcement Learning Bridging state and history representations: Understanding self-predictive rl, 2024

Reference 100

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source=arxiv_source observed=2026-08-06T23:34:41.090842Z digest=sha256:ee49ff18c63df5c8157a5e3ebb6b87af2b9b8b60f918bb0ed76f8e6a1a642a09

Observation eb4a3993-ec1e-41fd-9423-4d58fd144a9c · outbound

This paper cites Foundation policies with H ilbert representations.

A Survey of State Representation Learning for Deep Reinforcement Learning Foundation policies with H ilbert representations

Reference 101

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source=arxiv_source observed=2026-08-06T23:34:41.099059Z digest=sha256:98f150fc374f83d288ee35db46d37bd56f29b1e368f72728951e5161ad285b80

Pith citing papers

Observation ed9024be-4180-4384-af60-c34f0e2c26fc · inbound

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments cites this paper.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 14

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source=arxiv_source observed=2026-08-04T08:46:26.662381Z digest=sha256:73eca71fffbbac50f8a47432cde7e6f53df71eeb7d9c8b5cc696c6b5fb069904

Observation 563ba66e-ff96-4b01-9e1e-b05806f4cb76 · inbound

Belief-State RWKV for Reinforcement Learning under Partial Observability cites this paper.

Belief-State RWKV for Reinforcement Learning under Partial Observability A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 3

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arxiv_id, observed 2026-05-13T21:58:19.908240Z

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Observation 1919587a-f114-488e-a6e2-c5f4923b6f99 · inbound

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Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 5

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arxiv_id, observed 2026-05-12T07:16:26.328081Z

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Observation 6f8e3d58-0158-49b3-bc5a-a4fbcba1d4c5 · inbound

Abstraction for Offline Goal-Conditioned Reinforcement Learning cites this paper.

Abstraction for Offline Goal-Conditioned Reinforcement Learning A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 44

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arxiv_id, observed 2026-05-22T07:51:16.511799Z

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Observation b635bf3c-5141-4366-8881-ccae312cd0f5 · inbound

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning cites this paper.

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 14

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source=pdf_text observed=2026-07-12T13:42:27.758405Z digest=sha256:bc3e100c148e27f0ec9bfd27b8dfa4258e32c8c777c566a249a3e968c8d2820f

Observation 593b92c4-385f-447f-bcf0-ee48330d63b3 · inbound

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning cites this paper.

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 42

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arxiv_id, observed 2026-07-04T07:59:40.696101Z

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