Hybrid Cross-domain Robust Reinforcement Learning | Paper Citation Record

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:06.986317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:06.678932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:06.349343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:03:51.926044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:06.072621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:05.968363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:05.834644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:05.656151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:05.463189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:05.277973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:03:52.498739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:05.143507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:04.992162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:04.872205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:04.684468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:04.555221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:04.311896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:04.044759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:03.882049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:03.661930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:03.454026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:03.193874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:02.922280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:02.690470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:02.419331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:02.249818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:02.040841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:01.814857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:01.591693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:01.376539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

This paper cites Robust dynamic programming,.

Hybrid Cross-domain Robust Reinforcement Learning Robust dynamic programming,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:01.119170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:00.898945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:00.673085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:00.460610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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.

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 2026-08-07T06:34:17.273281+00:00.

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.

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 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers