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Paper Citation Record · LEDGER

Hybrid Cross-domain Robust Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2505.23003.

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

pith.paper-citation-record.v1
2505.23003 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:03:56.760781Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

63 of 63 outbound references displayed

  • verified exact1
  • verified fuzzy54
  • unresolved8
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87394822-1158-4e2a-87a6-348b9e263bd9 · outbound

This paper cites Human-level control through deep reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Human-level control through deep reinforcement learning,

Reference 1

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 8a273763-11b3-472c-952a-ba77d63aa8ee · outbound

This paper cites Mastering atari, go, chess and shogi by planning with a learned model,.

Hybrid Cross-domain Robust Reinforcement Learning Mastering atari, go, chess and shogi by planning with a learned model,

Reference 2

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 2fd1546c-ea6e-4d82-8626-dde48f253588 · outbound

This paper cites The limits and potentials of deep learning for robotics,.

Hybrid Cross-domain Robust Reinforcement Learning The limits and potentials of deep learning for robotics,

Reference 3

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 002a37b1-83d8-4b53-81ad-d11512796553 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Hybrid Cross-domain Robust Reinforcement Learning Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 48e32230-1d57-44db-b425-10a9c96f24d9 · outbound

This paper cites Mildly conservative q-learning for offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Mildly conservative q-learning for offline reinforcement learning,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 367462c2-33ff-4703-b988-82980a9bc3ba · outbound

This paper cites Morel: Model-based offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Morel: Model-based offline reinforcement learning,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation affdc510-2133-4651-982d-fd29ce0e93f1 · outbound

This paper cites A Conservative Approach for Few-Shot Transfer in Off- Dynamics Reinforcement Learning,.

Hybrid Cross-domain Robust Reinforcement Learning A Conservative Approach for Few-Shot Transfer in Off- Dynamics Reinforcement Learning,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 09fd12e4-4552-42df-8036-c29b447f1380 · outbound

This paper cites A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents,.

Hybrid Cross-domain Robust Reinforcement Learning A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e7d05e23-4de3-44f3-900e-bc388edabed5 · outbound

This paper cites OCEAN-MBRL: Offline Conservative Exploration for Model-Based Offline Reinforcement Learning,.

Hybrid Cross-domain Robust Reinforcement Learning OCEAN-MBRL: Offline Conservative Exploration for Model-Based Offline Reinforcement Learning,

Reference 9

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation c4838456-3f8f-40b5-8859-c6eb7ffa252a · outbound

This paper cites When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning,

Reference 10

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5fde591e-9809-4b8e-ad12-2313ea00fd07 · outbound

This paper cites H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps.

Hybrid Cross-domain Robust Reinforcement Learning H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 30a34d6f-bd9f-48d5-9f3d-c40f5d21773b · outbound

This paper cites DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning,.

Hybrid Cross-domain Robust Reinforcement Learning DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 9863a988-28be-49ba-85bb-7bd9587d266e · outbound

This paper cites Beyond ood state actions: Supported cross-domain offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Beyond ood state actions: Supported cross-domain offline reinforcement learning,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 148637c7-3901-48ea-a4e7-b42781ba9324 · outbound

This paper cites Contrastive Rep- resentation for Data Filtering in Cross-Domain Offline Reinforcement Learning,.

Hybrid Cross-domain Robust Reinforcement Learning Contrastive Rep- resentation for Data Filtering in Cross-Domain Offline Reinforcement Learning,

Reference 14

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation dd89e5db-4dc4-4870-8fa0-e7c3a389cd44 · outbound

This paper cites Off- Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers,.

Hybrid Cross-domain Robust Reinforcement Learning Off- Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers,

Reference 15

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation a76b4118-69e9-4131-85c0-7b2e7cad8f5f · outbound

This paper cites Policy Learning for Off-Dynamics RL with Deficient Support,.

Hybrid Cross-domain Robust Reinforcement Learning Policy Learning for Off-Dynamics RL with Deficient Support,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 3ad239e2-1cd4-45ff-9689-fa26d57387e1 · outbound

This paper cites Cross- domain policy adaptation via value-guided data filtering,.

Hybrid Cross-domain Robust Reinforcement Learning Cross- domain policy adaptation via value-guided data filtering,

Reference 17

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 02d113a7-65a0-41ba-ae77-7c7499ad273f · outbound

This paper cites Cross-Domain Policy Adaptation by Capturing Representation Mismatch,.

Hybrid Cross-domain Robust Reinforcement Learning Cross-Domain Policy Adaptation by Capturing Representation Mismatch,

Reference 18

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 7da39e0c-304f-4272-994f-29a4ef906acb · outbound

This paper cites Sim-to-real transfer of robotic control with dynamics randomization,.

Hybrid Cross-domain Robust Reinforcement Learning Sim-to-real transfer of robotic control with dynamics randomization,

Reference 19

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 145cbc1a-6d94-4d4b-b850-8b08b5f3167e · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world,.

Hybrid Cross-domain Robust Reinforcement Learning Domain randomization for transferring deep neural networks from simulation to the real world,

Reference 20

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 32e376c8-f2e8-44c5-b9de-45dcffe9276f · outbound

This paper cites CAD2RL: Real Single-Image Flight Without a Single Real Image,.

Hybrid Cross-domain Robust Reinforcement Learning CAD2RL: Real Single-Image Flight Without a Single Real Image,

Reference 21

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation c9fb2cf2-adc1-4e99-9760-fa4483dbd27a · outbound

This paper cites Neural networks for control and system identification,.

Hybrid Cross-domain Robust Reinforcement Learning Neural networks for control and system identification,

Reference 22

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 9c911f14-a743-456c-a728-989bd090d3d9 · outbound

This paper cites Fast model identification via physics engines for data-efficient policy search,.

Hybrid Cross-domain Robust Reinforcement Learning Fast model identification via physics engines for data-efficient policy search,

Reference 23

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 1a20c7f0-0000-405a-9f1a-227ddeb4d42f · outbound

This paper cites Closing the sim-to-real loop: Adapting simulation randomization with real world experience,.

Hybrid Cross-domain Robust Reinforcement Learning Closing the sim-to-real loop: Adapting simulation randomization with real world experience,

Reference 24

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 6eea4444-5e06-4c27-92c9-c004d784e2b6 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Hybrid Cross-domain Robust Reinforcement Learning Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 25

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 580266bb-4c62-4cdf-846c-8794ae4b81a2 · outbound

This paper cites Learning to Adapt in Dynamic, Real-World Environments through Meta- Reinforcement Learning,.

Hybrid Cross-domain Robust Reinforcement Learning Learning to Adapt in Dynamic, Real-World Environments through Meta- Reinforcement Learning,

Reference 26

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5ce9a842-9c91-4969-b833-f743813ed8bd · outbound

This paper cites Zero-shot policy transfer with disentangled task representation of meta- reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Zero-shot policy transfer with disentangled task representation of meta- reinforcement learning,

Reference 27

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation c68b46a9-de9d-43a5-ac8d-94f6ba9bd43d · outbound

This paper cites Provably good batch off- policy reinforcement learning without great exploration,.

Hybrid Cross-domain Robust Reinforcement Learning Provably good batch off- policy reinforcement learning without great exploration,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 59de49a2-ba08-4b3b-9e75-fa038ee6f5aa · outbound

This paper cites Conservative q-learning for offline re- inforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Conservative q-learning for offline re- inforcement learning,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 3c564a64-660a-468b-84fb-8d5875d12b02 · outbound

This paper cites Rambo-rl: Robust adversarial model-based offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Rambo-rl: Robust adversarial model-based offline reinforcement learning,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:53.855773Z digest=sha256:5b8e0ca5a674b1493f36f8541d1e67e5e84fc5b722e507b1b6674024811cabdf

Observation b13760c2-771e-4d62-a5a2-2f8a37e9708c · outbound

This paper cites Robust dynamic programming,.

Hybrid Cross-domain Robust Reinforcement Learning Robust dynamic programming,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 26094e38-8cbf-4fdd-b247-a7c846d25e51 · outbound

This paper cites Robust control of Markov decision processes with uncer- tain transition matrices,.

Hybrid Cross-domain Robust Reinforcement Learning Robust control of Markov decision processes with uncer- tain transition matrices,

Reference 32

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e86b3880-c6eb-41b1-95b6-9a4b3ac32919 · outbound

This paper cites Distributionally robust Markov decision processes,.

Hybrid Cross-domain Robust Reinforcement Learning Distributionally robust Markov decision processes,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.110255Z digest=sha256:b120be4fc21bbb83ee889b4c16616f1f5b747177646040b481517118470c9321

Observation 873468c7-91db-4ae5-9636-29fed1e512db · outbound

This paper cites Policy gradient method for robust reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Policy gradient method for robust reinforcement learning,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.242757Z digest=sha256:4c85bbfea1c8d0d8fcb2943db29b90f5d6f8473769291e4b2368dae8f489609f

Observation 6f1da6b5-b889-49a8-8390-6e3e00541838 · outbound

This paper cites Policy Gradient in Robust MDPs with Global Convergence Guarantee.

Hybrid Cross-domain Robust Reinforcement Learning Policy Gradient in Robust MDPs with Global Convergence Guarantee

Reference 35

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no resolver link, observed 2026-08-07T13:03:54.312568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:03:54.312568Z digest=sha256:a8a151a4a8d6238d2845ef7873281bd0efab64cf2040a1cfe5701682cc39dfdc

Observation 3495fd71-931c-4174-9809-1c43c14856cf · outbound

This paper cites Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics,.

Hybrid Cross-domain Robust Reinforcement Learning Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-07T13:04:00.292564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.394415Z digest=sha256:984a6b1ad50c839999e52b78d1bc98deaa9df4edae4a1164152b758b79e363dd

Observation a645f45b-6780-4488-b6bc-314e8be9efa1 · outbound

This paper cites Improved sample complexity bounds for distri- butionally robust reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Improved sample complexity bounds for distri- butionally robust reinforcement learning,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:04:00.108759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.478229Z digest=sha256:388cc300b0ef3b0691d5a3cee57ff0d8327f2327387e9c3c11ad2cf13aaa34bf

Observation 369eb0e5-810c-41d8-9b61-7a9e48e8c098 · outbound

This paper cites Online robust reinforcement learning with model uncertainty,.

Hybrid Cross-domain Robust Reinforcement Learning Online robust reinforcement learning with model uncertainty,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:59.946253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.549558Z digest=sha256:36d0be50fa4b14c75fbc5569ec83cdd3ed23ec1136f4352cf54e87ccf72b2c6d

Observation 49f95e52-ae19-4839-b84e-00120c6e8c93 · outbound

This paper cites Online Policy Optimization for Robust MDP.

Hybrid Cross-domain Robust Reinforcement Learning Online Policy Optimization for Robust MDP

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T13:03:54.632287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:03:54.632287Z digest=sha256:10cbe250fe3b9e26f054fe92b3a8bdcd22531a29b6a3e650c3105ed048d461d4

Observation 1d4a5a95-50c4-4cc0-9296-6ae259515ffb · outbound

This paper cites Finite-sample re- gret bound for distributionally robust offline tabular reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Finite-sample re- gret bound for distributionally robust offline tabular reinforcement learning,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:59.744758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.736585Z digest=sha256:61cdd2961a7d9cc50c03833f6f5fefb78b1cddd8f3f550ed93a6f64e9f48279b

Observation 03022763-18cb-4a4d-9113-1f771d41a8e7 · outbound

This paper cites Robust reinforcement learning using offline data,.

Hybrid Cross-domain Robust Reinforcement Learning Robust reinforcement learning using offline data,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:59.635353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.815160Z digest=sha256:9e69e368be507b1834b90b647cdf4acc1f140e351224f5b78e3e2f0ded34f4bb

Observation 56dad26c-385e-40dd-802d-5074168bff4a · outbound

This paper cites Learning models with uniform performance via distri- butionally robust optimization,.

Hybrid Cross-domain Robust Reinforcement Learning Learning models with uniform performance via distri- butionally robust optimization,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:59.465546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:54.881160Z digest=sha256:bb3918043f6a68121071b66a72383b07740e5a1c86b4327588b7f07f84a9d990

Observation c8cd894a-0807-4521-b6a3-3d3e495534f1 · outbound

This paper cites Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity.

Hybrid Cross-domain Robust Reinforcement Learning Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity

Reference 43

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unresolved
no resolver link, observed 2026-08-07T13:03:54.976956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:03:54.976956Z digest=sha256:df88c6ed58387a04cca590efc2a351e06cae151a82cc8630680212ec28db2c51

Observation b34cba95-4416-4216-b7b1-bb5a0b928fc9 · outbound

This paper cites Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation.

Hybrid Cross-domain Robust Reinforcement Learning Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:03:57.022169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.069175Z digest=sha256:a544e9b1c370430626dd480ef329040a0f2f490907d6fb2c6efe1e3edc3f6fea

Observation 41e46250-17b2-4f09-b02c-d4b8c80b38e7 · outbound

This paper cites Double pessimism is provably effi- cient for distributionally robust offline reinforcement learning: Generic algorithm and robust partial coverage,.

Hybrid Cross-domain Robust Reinforcement Learning Double pessimism is provably effi- cient for distributionally robust offline reinforcement learning: Generic algorithm and robust partial coverage,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:59.332299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.166807Z digest=sha256:02b4867ce41e1c4af39bdbad3821f7fdb6afb007f91cb15ec0a470bf9c92934d

Observation 48ea1df1-0783-4823-a819-bd7e56b38e09 · outbound

This paper cites Using simulation and domain adaptation to improve efficiency of deep robotic grasping,.

Hybrid Cross-domain Robust Reinforcement Learning Using simulation and domain adaptation to improve efficiency of deep robotic grasping,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:59.209500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.260086Z digest=sha256:a5a092b73ccf18c427cae8347e69741cd97560805863338f4c470e4f4d6d0518

Observation a4b886d6-de1f-4d63-bf24-368515875f3c · outbound

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

Hybrid Cross-domain Robust Reinforcement Learning Darla: Improving zero-shot transfer in reinforcement learning,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:59.063394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.384899Z digest=sha256:ccf0dbda2ab265d2efae64a540211275c18db5408ac2ce9e334314bf91692d3a

Observation 8e15cec3-3307-469b-9806-3a2877d65008 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Hybrid Cross-domain Robust Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T13:03:55.466436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:03:55.466436Z digest=sha256:adfcd065a1144d5bb1e55bd652d64dd6510508397bfd9b6a43e32f2415fac31e

Observation c59f9d8e-47e3-4e64-afad-b54722738100 · outbound

This paper cites Mopo: Model-based offline policy optimization,.

Hybrid Cross-domain Robust Reinforcement Learning Mopo: Model-based offline policy optimization,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.973425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.540687Z digest=sha256:994fbb9d34c6eabcd0724eb36f8b5ad654f29aa9e125d5dbeff04314e236b5ec

Observation afe29559-3ad9-4b6b-a139-6719ce0d4608 · outbound

This paper cites Robust adversarial reinforce- ment learning,.

Hybrid Cross-domain Robust Reinforcement Learning Robust adversarial reinforce- ment learning,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.835115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.636939Z digest=sha256:523e15b205e7cab80c89b2e7d2482c49ae6c45e10914d83cec655078b564063f

Observation 81d67441-b454-4d96-a0e1-162ab450fdb3 · outbound

This paper cites Exponential bellman equation and im- proved regret bounds for risk-sensitive reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Exponential bellman equation and im- proved regret bounds for risk-sensitive reinforcement learning,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.699419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.672126Z digest=sha256:12471183b2f3976789d10b642a10e54536984b5dd508e38b50e7feda9e10c971

Observation 50448d80-034c-4d4f-b16c-face20210058 · outbound

This paper cites One risk to rule them all: A risk-sensitive perspective on model-based offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning One risk to rule them all: A risk-sensitive perspective on model-based offline reinforcement learning,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.610582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.758678Z digest=sha256:f8076edf12ae24a3b789451ba2d3832fa7b2786c530938c0cc8f68ffa169c5a9

Observation 435bff98-ffe5-417d-90ea-eb3073ccf41d · outbound

This paper cites Corruption-robust offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Corruption-robust offline reinforcement learning,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.451377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.913745Z digest=sha256:909da20989c0929c057e05549674b81a4a843363a7ce09109ee5335ddc4aa691

Observation c21a02b3-c07a-4a0d-9b38-4d1252472888 · outbound

This paper cites Corruption-robust offline reinforcement learning with general function approximation,.

Hybrid Cross-domain Robust Reinforcement Learning Corruption-robust offline reinforcement learning with general function approximation,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.317081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:55.982637Z digest=sha256:d0aaf26c9e690399efd49b6df11fffc75fc9d9a4d068489cfb8a37dddf991214

Observation a2d6c9af-417d-42bc-b9ff-29439b77f3f2 · outbound

This paper cites Distributionally robust stochastic programming,.

Hybrid Cross-domain Robust Reinforcement Learning Distributionally robust stochastic programming,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.215785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:56.038538Z digest=sha256:8cf91d3d4a0f7ba080bdcee3704a23900ab8ff45e682823d66c2a168ada37d21

Observation 0af4f2ac-0178-4c0c-ba3b-b66a8bc1d4f7 · outbound

This paper cites Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees,.

Hybrid Cross-domain Robust Reinforcement Learning Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:58.100122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:56.144266Z digest=sha256:393d84fe770f0de4ecf814740e3d0333655c6b58bb8197429ef5e2af6e9c164f

Observation f8fd7989-8a91-4d0f-8422-8aeaca0fd5e8 · outbound

This paper cites Deep reinforcement learning in a handful of trials using probabilistic dynamics models,.

Hybrid Cross-domain Robust Reinforcement Learning Deep reinforcement learning in a handful of trials using probabilistic dynamics models,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:57.940029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:56.272869Z digest=sha256:76a222c954a2d30fad4af26b0a9f08112cea1ff534ad55f20f8fb08974c3d6b1

Observation 71c50236-d4b2-4e64-9896-da802303d253 · outbound

This paper cites Model-Bellman inconsistency for model-based offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Model-Bellman inconsistency for model-based offline reinforcement learning,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:57.745498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:56.341894Z digest=sha256:d0854866b29df9e7b1bf1b07184cf44c849704ef5b80407ffce655e56e43f77c

Observation 698b2d49-2b42-478f-a274-f2f6dd91adf3 · outbound

This paper cites Uncertainty-driven trajectory truncation for data augmentation in offline reinforcement learning,.

Hybrid Cross-domain Robust Reinforcement Learning Uncertainty-driven trajectory truncation for data augmentation in offline reinforcement learning,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:57.606895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:56.426774Z digest=sha256:32f41134c3ce59a30b4c46b348524c2397fa9df5ef44a4b0a214fd334d504842

Observation ac45104f-9f24-472f-b7e2-db601b000a2b · outbound

This paper cites Prioritized Experience Replay.

Hybrid Cross-domain Robust Reinforcement Learning Prioritized Experience Replay

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T13:03:56.555963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:03:56.555963Z digest=sha256:192d67d00d0240cf72f31b359b2821c2eaf5cb2f65dffd0d00492375d1dc86f5

Observation e63780ca-5c5c-49d7-966c-9d136d33e92a · outbound

This paper cites labml.ai Annotated Paper Implementations,.

Hybrid Cross-domain Robust Reinforcement Learning labml.ai Annotated Paper Implementations,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:57.446148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:56.620245Z digest=sha256:38e2d5e4f5112423e55d3ec8b76ad02a3a4eabf38088904fbd914d71ff621145

Observation 37130817-48e1-4fa4-8085-b305024b6914 · outbound

This paper cites REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer.

Hybrid Cross-domain Robust Reinforcement Learning REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T13:03:56.703511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:03:56.703511Z digest=sha256:973d14d3540f27a04c37cb649f28ca49ec3846f80c5563a673489d65f8c596e9

Observation 0a77678a-1738-4910-8d0f-efec2c3f7d5a · outbound

This paper cites -m": multi comp, “-s.

Hybrid Cross-domain Robust Reinforcement Learning -m": multi comp, “-s

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:03:57.237850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T13:03:56.760781Z digest=sha256:7182ad081695d48488f59f4ae73bbc616898a8071b9ebc64ccb68b16bd6b3220

Pith citing papers

No inbound Pith citation observations are available.