Paper Citation Record · LEDGER

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2502.01268.

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

pith.paper-citation-record.v1
2502.01268 v1

Coverage vector

measured 45 of 45 reference resolution

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measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T13:48:08.135538Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T14:10:29.768184Z

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3f54b14-d710-4f71-bc6c-3cbe47708583 · outbound

This paper cites Machine learning for large-scale optimization in 6G wireless networks,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Machine learning for large-scale optimization in 6G wireless networks,

Reference 1

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Observation 3473563e-6f9b-41e4-a589-9c72a1f12d7f · outbound

This paper cites Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges,

Reference 2

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Observation 075f1611-0f64-47aa-9271-d111534927f0 · outbound

This paper cites Reinforcement learning: An introduction,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Reinforcement learning: An introduction,

Reference 3

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This paper cites Self- organization in small cell networks: A reinforcement learning approach,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Self- organization in small cell networks: A reinforcement learning approach,

Reference 4

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Observation b554bd24-4f1d-4ce1-9915-6fb80c34973c · outbound

This paper cites Deep reinforcement learning for resource management in network slicing,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Deep reinforcement learning for resource management in network slicing,

Reference 5

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Observation c45f0926-088b-4fc8-b6bf-8c17b0e1cb03 · outbound

This paper cites Multi-UA V path learning for age and power optimization in IoT with UA V battery recharge,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Multi-UA V path learning for age and power optimization in IoT with UA V battery recharge,

Reference 6

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Observation df486839-0997-4d33-bcb1-2bebd811bb29 · outbound

This paper cites Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow’s inter- sections,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow’s inter- sections,

Reference 7

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Observation 10dfdac9-e858-4510-906e-ff181a74f38e · outbound

This paper cites Applications of deep reinforcement learning in communications and networking: A survey,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Applications of deep reinforcement learning in communications and networking: A survey,

Reference 8

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Observation 59502844-32ff-47af-950b-7ff5b8163c2f · outbound

This paper cites Deep reinforcement learning for internet of things: A comprehensive survey,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Deep reinforcement learning for internet of things: A comprehensive survey,

Reference 9

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Observation cc805f01-a82b-4921-9759-f321e93cce5d · outbound

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

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Human-level control through deep reinforce- ment learning,

Reference 10

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Observation 4e63dac5-dae6-4e15-aa8a-247b2322b396 · outbound

This paper cites Exploring the YOLO-FT deep learning algorithm for UA V- based smart agriculture detection in communication networks,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Exploring the YOLO-FT deep learning algorithm for UA V- based smart agriculture detection in communication networks,

Reference 11

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Observation cb1549d8-c2c0-41f3-8358-0d4f20bc9b3e · outbound

This paper cites Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges,

Reference 12

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Observation 50a73782-49e9-4b86-b2ee-ac34201f693f · outbound

This paper cites Leveraging precision agriculture techniques using UA Vs and emerging disruptive technologies,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Leveraging precision agriculture techniques using UA Vs and emerging disruptive technologies,

Reference 13

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Observation bf948ad7-c8a1-448d-98bb-d37d8355e71d · outbound

This paper cites AoI- Aware Energy-Efficient SFC in UA V-Aided Smart Agriculture Using Asynchronous Federated Learning,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning AoI- Aware Energy-Efficient SFC in UA V-Aided Smart Agriculture Using Asynchronous Federated Learning,

Reference 14

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Observation 3091308a-66dc-4ee1-ba24-5f35d6b05d15 · outbound

This paper cites Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications,

Reference 15

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Observation b2605cd7-9df9-435e-93d5-896ae1b0c3d5 · outbound

This paper cites IoT-aerial base station task offloading with risk-sensitive reinforcement learning for smart agriculture,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning IoT-aerial base station task offloading with risk-sensitive reinforcement learning for smart agriculture,

Reference 16

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Observation 6cbf417a-58a0-4125-bde8-502166ddfc27 · outbound

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

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 17

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This paper cites Distributed learning methodologies for massive machine type commu- nication,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Distributed learning methodologies for massive machine type commu- nication,

Reference 18

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Observation f559d1ed-1a84-4fc7-b4e9-0dde2a503587 · outbound

This paper cites Comeback kid: Resilience for mixed-critical wireless network resource management,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Comeback kid: Resilience for mixed-critical wireless network resource management,

Reference 19

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Observation 598cb1b6-834a-47f4-8214-01d995959009 · outbound

This paper cites Edge-IoT-UA V Adaptation To- ward Precision Agriculture Using 3D-LiDAR Point Clouds,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Edge-IoT-UA V Adaptation To- ward Precision Agriculture Using 3D-LiDAR Point Clouds,

Reference 20

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Observation 4e21aab3-3fc4-4977-8db0-97043b83fd83 · outbound

This paper cites Conservative Q-learning for offline reinforcement learning,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Conservative Q-learning for offline reinforcement learning,

Reference 21

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Observation a3135110-aafc-44d9-a5dd-140f3b9c0c78 · outbound

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Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Offline and distributional reinforcement learn- ing for wireless communications,

Reference 22

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Observation 79aa9432-4dbc-4cb3-9548-29ef2d86c6ab · outbound

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

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 23

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Observation c56cd229-d549-43a8-9473-57070d112973 · outbound

This paper cites 3D UA V trajectory and data collection optimisation via deep reinforcement learning,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning 3D UA V trajectory and data collection optimisation via deep reinforcement learning,

Reference 24

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Observation 39d1d8f2-e2bb-4978-91fa-9f2253fe22ea · outbound

This paper cites Deep reinforce- ment learning based resource allocation and trajectory planning in inte- grated sensing and communications UA V network,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Deep reinforce- ment learning based resource allocation and trajectory planning in inte- grated sensing and communications UA V network,

Reference 25

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Observation 81c62d73-7316-43b3-ac73-7b0c89b5a1cf · outbound

This paper cites Deep reinforcement learning and NOMA-based multi-objective RIS-assisted IS-UA V-TNs: Trajectory optimization and beamforming design,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Deep reinforcement learning and NOMA-based multi-objective RIS-assisted IS-UA V-TNs: Trajectory optimization and beamforming design,

Reference 26

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Observation a679a8a0-bb91-4af4-8bbc-5729b3508a34 · outbound

This paper cites Meta-learning to communicate: Fast end-to-end training for fading channels,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Meta-learning to communicate: Fast end-to-end training for fading channels,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.585088Z

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Observation be2441b0-6c5c-45e4-913b-3bd1e92f023b · outbound

This paper cites Transfer learning and meta learning-based fast downlink beamforming adapta- tion,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Transfer learning and meta learning-based fast downlink beamforming adapta- tion,

Reference 28

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Observation df0b7ae8-a792-4968-b59a-17d00f097f79 · outbound

This paper cites MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion

Reference 29

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Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Meta-reinforcement learning based resource allocation for dynamic V2X communications,

Reference 30

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Observation e566b3f6-1ca1-4101-8f2f-941ed444c0c4 · outbound

This paper cites Distributed multi- agent meta learning for trajectory design in wireless drone networks,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Distributed multi- agent meta learning for trajectory design in wireless drone networks,

Reference 31

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This paper cites Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks

Reference 32

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This paper cites Conservative and risk-aware offline multi-agent reinforcement learning for digital twins,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Conservative and risk-aware offline multi-agent reinforcement learning for digital twins,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.549079Z

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Observation bedd48e1-423f-46d5-8914-1c18721f0e47 · outbound

This paper cites Offline reinforcement learning for wireless network optimization with mixture datasets,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Offline reinforcement learning for wireless network optimization with mixture datasets,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.539003Z

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Observation a153a9db-070d-46c7-9a1d-018bf8216fba · outbound

This paper cites Offline and Distributional Reinforcement Learning for Radio Resource Management.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Offline and Distributional Reinforcement Learning for Radio Resource Management

Reference 35

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Observation 9f306572-e2e7-414a-83fb-9e47fd72be58 · outbound

This paper cites An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-09T15:55:04.404438Z

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Observation f227a4e3-4ba1-44e3-aa12-f41b176363f0 · outbound

This paper cites Deep reinforcement learning for fresh data collection in UA V-assisted IoT networks,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Deep reinforcement learning for fresh data collection in UA V-assisted IoT networks,

Reference 37

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This paper cites Traffic learning and proactive UA V trajectory planning for data uplink in markovian IoT models,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Traffic learning and proactive UA V trajectory planning for data uplink in markovian IoT models,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.522130Z

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Observation 515eb2cd-b0ad-42f4-a000-de909051609f · outbound

This paper cites Path loss models for outdoor environment-with a focus on rain attenuation impact on short-range millimeter-wave links,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Path loss models for outdoor environment-with a focus on rain attenuation impact on short-range millimeter-wave links,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.512106Z

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Observation 72cf1d27-76c0-4126-bb3f-6469565750e5 · outbound

This paper cites Impact of UA V failure and severe weather conditions in mmWave and terahertz signals for aerial edge computing,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Impact of UA V failure and severe weather conditions in mmWave and terahertz signals for aerial edge computing,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.502608Z

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Observation cca51b4b-5a43-4aa9-8057-d2bbeb517131 · outbound

This paper cites Specific attenuation model for rain for use in prediction methods,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Specific attenuation model for rain for use in prediction methods,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.492217Z

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Observation e0e4ec26-1239-4329-bf7b-9366be546c21 · outbound

This paper cites Meteorologically introduced impacts on aerial channels and UA V communications,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Meteorologically introduced impacts on aerial channels and UA V communications,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.482270Z

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Observation d468aa2f-3938-4b64-bdbf-c1121ee33ad2 · outbound

This paper cites Implicit quantile networks for distributional reinforcement learning,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Implicit quantile networks for distributional reinforcement learning,

Reference 43

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Observation baf289ab-235f-4622-9104-b7dec734bf16 · outbound

This paper cites Semantic meta-split learning: A tinyml scheme for few-shot wireless image classification,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Semantic meta-split learning: A tinyml scheme for few-shot wireless image classification,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.466611Z

Source-reported events for the cited work

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Observation 958b0f7c-768f-4020-9988-b4fa4679bbca · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library,.

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Pytorch: An imperative style, high- performance deep learning library,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:55:04.456598Z

Source-reported events for the cited work

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Pith citing papers

Observation 12f25707-abed-4d3f-b381-46c81d683d44 · inbound

Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap cites this paper.

Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning

Reference 168

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:10:29.769550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.