Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2510.19244.

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

pith.paper-citation-record.v1
2510.19244 v3

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:46:26.728164Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy0
unresolved39
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malformed identifier0
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External citation measurements

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Outbound references

Observation 8b3257b1-52fc-49a4-ae6b-788b2213cc33 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments , " * write output.state after.block = add.period write newline

Reference 1

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Observation 362f6e4c-6384-4201-aeea-b546682cea6f · outbound

This paper cites write newline.

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

Reference 2

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Observation cd2512e0-79df-4cba-bd8e-2b5c9981cc77 · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 3

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Observation b9b7391a-ce56-44d2-971f-3435bb785123 · outbound

This paper cites Surrogate Fitness Metrics for Interpretable Reinforcement Learning.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Surrogate Fitness Metrics for Interpretable Reinforcement Learning

Reference 4

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Observation f43c197e-732c-484c-ac87-8c34257d5eee · outbound

This paper cites M.; and S im s ek, \"O.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments M.; and S im s ek, \"O

Reference 6

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Observation 8fafc73c-94ae-4d1c-83ea-ff5f1f9de63f · outbound

This paper cites A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values

Reference 7

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Observation 466d44f1-46bb-459b-b397-dd11c561836f · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 8

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Observation 4215f0bb-b83a-415c-b9d3-b3c9b386ecce · outbound

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

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Vision-Language Models Provide Promptable Representations for Reinforcement Learning

Reference 9

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Observation 0712794e-c8e0-494b-bdb3-83cfa4fc9922 · outbound

This paper cites A Survey on Explainable Deep Reinforcement Learning.

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 on Explainable Deep Reinforcement Learning

Reference 10

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Observation d77278bd-decf-4c06-aec8-1b42732f1e48 · outbound

This paper cites G.; P \'e rez-Aracil, J.; Salcedo-Sanz, S.; and Pedreira, C.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments G.; P \'e rez-Aracil, J.; Salcedo-Sanz, S.; and Pedreira, C

Reference 11

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Observation 000a4127-7276-43f3-a03a-079f4c9ae5dc · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T08:46:26.657274Z

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Observation 532d6a33-4a7a-46a5-a2c2-9d631e8354be · outbound

This paper cites Counterfactual Explanations for Continuous Action Reinforcement Learning.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Counterfactual Explanations for Continuous Action Reinforcement Learning

Reference 13

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

This paper cites A Survey of State Representation Learning for Deep Reinforcement Learning.

Reference 14

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Observation 28070c0e-c9f8-453e-9822-c86f5b06c55d · outbound

This paper cites Shapley explainability on the data manifold.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Shapley explainability on the data manifold

Reference 15

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Observation 3d3286d2-e69f-4c69-8e80-bac2839c1aa8 · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 16

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Observation 63cc7d27-472a-4830-b41f-27794148edbf · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 17

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Observation 470c8c2d-c8a7-42bf-af7b-a1837c438abd · outbound

This paper cites S.; Claessens, B.; and Develder, C.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments S.; Claessens, B.; and Develder, C

Reference 18

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Observation 1dfff01c-962b-46a7-8e57-0fdcc2ce48f3 · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 19

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Observation 812d1326-7e05-455f-aaa1-76ed775978b8 · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 20

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Observation 51a5d485-54ad-43a5-9c59-528b3d08c80d · outbound

This paper cites W.; and Liang, P.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments W.; and Liang, P

Reference 21

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Observation 79dd1d11-5e3c-4c8f-992c-b5e6808738fd · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs

Reference 22

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Observation 464d1cbb-98f9-4ce7-a4be-20f7b31a4141 · outbound

This paper cites From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation

Reference 23

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Observation 76ee77d2-9276-4483-89a8-51502b16a82a · outbound

This paper cites M.; and Lee, S.-I.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments M.; and Lee, S.-I

Reference 24

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 25

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Observation a2c587ab-27ea-4bae-a820-883fa1d9302a · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Y.; Hasan, M

Reference 26

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Observation ee34eeb7-c300-4226-a1d8-ee845a2627a2 · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 27

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Observation 49e7af5e-9361-4e2d-a518-40299d9ac1ab · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 28

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Observation 535a25e9-8e80-48db-9141-25e5b5be310a · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments D., Anurag Koul; and Fern, A

Reference 29

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Observation e1bf4d76-6509-42d2-9c2e-88f700a68b26 · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Grounded Reinforcement Learning for Visual Reasoning

Reference 30

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 31

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 32

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 33

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 34

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Observation d9a8cb9c-3c03-46a6-9272-10d1995a6e90 · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments S.; and Barto, A

Reference 35

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Observation 8f6ea3a5-230e-47a8-96af-764fc33aecd0 · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 36

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Observation fada0d9f-ef3e-4cb8-9cf1-557163108409 · outbound

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

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Observation 445f7345-a679-4281-874e-67fd700147d0 · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

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Observation aa332c7a-21c0-4fae-a253-36a0563f48e2 · outbound

This paper cites an unresolved cited work.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Unresolved cited work

Reference 40

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Pith citing papers

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