Paper Citation Record · LEDGER

How to craft a deep reinforcement learning policy for wind farm flow control

As of 7 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.06204.

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

pith.paper-citation-record.v1
2506.06204 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:02:54.914397Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

29 of 29 outbound references displayed

verified exact5
verified fuzzy9
unresolved10
parse uncertain0
malformed identifier3
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e872866-a27d-49e2-baed-901684e131ef · outbound

This paper cites Field test of wake steering at an offshore wind farm.

How to craft a deep reinforcement learning policy for wind farm flow control Field test of wake steering at an offshore wind farm

Reference 1

Resolution: verified exact; doi, observed 2026-08-07T06:02:54.987519Z

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 6820eaa8-c5b0-42ef-8308-e44d0055f2b6 · outbound

This paper cites Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions.

How to craft a deep reinforcement learning policy for wind farm flow control Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions

Reference 2

Resolution: malformed identifier; no resolver link, observed 2026-08-07T06:02:54.840038Z

Source-reported events for the cited work

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Observation ffd8ee7b-bf7b-4e2a-be20-e87f2697a716 · outbound

This paper cites Data-driven wind farm flow control and challenges towards field imple- mentation: A review.

How to craft a deep reinforcement learning policy for wind farm flow control Data-driven wind farm flow control and challenges towards field imple- mentation: A review

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.458563Z

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 1e66069d-a251-4dae-b562-0dc58f0860e3 · outbound

This paper cites Model-free closed-loop wind farm control using reinforcement learning with recursive least squares.

How to craft a deep reinforcement learning policy for wind farm flow control Model-free closed-loop wind farm control using reinforcement learning with recursive least squares

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.444564Z

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 948aa78a-b9e2-4a32-956f-6f739aa925b8 · outbound

This paper cites Actor Critic Agents for Wind Farm Control.

How to craft a deep reinforcement learning policy for wind farm flow control Actor Critic Agents for Wind Farm Control

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.848817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3f01cca-751e-4672-bdc7-a9d52ce5fa01 · outbound

This paper cites Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations.

How to craft a deep reinforcement learning policy for wind farm flow control Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations

Reference 6

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T06:02:55.261065Z

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 cc3e1c3d-e171-4de9-8600-ff989a0bcc4e · outbound

This paper cites A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization*.

How to craft a deep reinforcement learning policy for wind farm flow control A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization*

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.854412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce1f2929-7248-485c-8571-09aeb04598f8 · outbound

This paper cites Deep Reinforcement Learning for Active Wake Control.

How to craft a deep reinforcement learning policy for wind farm flow control Deep Reinforcement Learning for Active Wake Control

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.434468Z

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 571fcacc-e752-42f6-93f4-a9e8b448d319 · outbound

This paper cites MARLYC: Multi-Agent Reinforcement Learning Yaw Control.

How to craft a deep reinforcement learning policy for wind farm flow control MARLYC: Multi-Agent Reinforcement Learning Yaw Control

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T06:02:55.154467Z

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 f7ad9f19-f134-49fa-a601-edb0779ba1ca · outbound

This paper cites Deep reinforcement learning-based adaptive yaw control for wind farms in fluctuating winds.

How to craft a deep reinforcement learning policy for wind farm flow control Deep reinforcement learning-based adaptive yaw control for wind farms in fluctuating winds

Reference 10

Resolution: verified exact; doi, observed 2026-08-07T06:02:54.974769Z

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 b805a6ff-7de4-4dc1-9178-36684e2ce92b · outbound

This paper cites FALCON- FArm Level CONtrol for wind turbines using multi-agent deep reinforcement learning.

How to craft a deep reinforcement learning policy for wind farm flow control FALCON- FArm Level CONtrol for wind turbines using multi-agent deep reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.422106Z

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 bbd4f65b-34b6-4350-9e42-07bc927a28e1 · outbound

This paper cites Learning to optimise wind farms with graph transformers.

How to craft a deep reinforcement learning policy for wind farm flow control Learning to optimise wind farms with graph transformers

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-07T06:02:54.869641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aadb3af9-abc8-4e3d-afce-507514f3da6d · outbound

This paper cites Graph Attention Networks.

How to craft a deep reinforcement learning policy for wind farm flow control Graph Attention Networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.413143Z

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 50a8fd80-d88a-4faa-9d80-450556132687 · outbound

This paper cites Attention is All you Need.

How to craft a deep reinforcement learning policy for wind farm flow control Attention is All you Need

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.399659Z

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 4485cbf6-08ee-4e6c-9e2d-6b5d91e5f5ad · outbound

This paper cites an unresolved cited work.

How to craft a deep reinforcement learning policy for wind farm flow control Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:02:55.385499Z

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 28ffbb84-c1b3-43b9-b0d5-b5f36ae7a7ec · outbound

This paper cites Serial-Refine Method for Fast Wake-Steering Yaw Optimization.

How to craft a deep reinforcement learning policy for wind farm flow control Serial-Refine Method for Fast Wake-Steering Yaw Optimization

Reference 16

Resolution: malformed identifier; no resolver link, observed 2026-08-07T06:02:54.879641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 332baf39-449f-4c98-92c7-39a7da0fa8c6 · outbound

This paper cites On the importance of wind predictions in wake steering optimization.

How to craft a deep reinforcement learning policy for wind farm flow control On the importance of wind predictions in wake steering optimization

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T06:02:54.948234Z

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 18ccfe4b-619b-45b6-adcf-2f5c2b029d48 · outbound

This paper cites Proximal Policy Optimization Algorithms.

How to craft a deep reinforcement learning policy for wind farm flow control Proximal Policy Optimization Algorithms

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.885113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64e0bcf-b532-4791-b3ee-68d21a1695b3 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

How to craft a deep reinforcement learning policy for wind farm flow control High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.888461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b5d8a8b-a64b-4e31-954f-58b6ba5c584f · outbound

This paper cites Control-oriented model for secondary effects of wake steering.

How to craft a deep reinforcement learning policy for wind farm flow control Control-oriented model for secondary effects of wake steering

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.891292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1e6d33d-675e-4881-bd61-57b0599df298 · outbound

This paper cites IEA Wind TCP Task 37: Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine.

How to craft a deep reinforcement learning policy for wind farm flow control IEA Wind TCP Task 37: Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine

Reference 21

Resolution: verified exact; raw_fallback, observed 2026-08-07T06:02:55.070259Z

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 491e3c3b-3fd9-472b-9347-c0cc8992f4b2 · outbound

This paper cites Stable-Baselines3: Reliable Reinforcement Learning Implementations.

How to craft a deep reinforcement learning policy for wind farm flow control Stable-Baselines3: Reliable Reinforcement Learning Implementations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.375334Z

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 af40d9f0-db23-425c-8fce-69761c8de7bd · outbound

This paper cites RLlib: Abstractions for Distributed Reinforcement Learning.

How to craft a deep reinforcement learning policy for wind farm flow control RLlib: Abstractions for Distributed Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.899284Z

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Unavailable: canonical work link unavailable.

Observation dac90711-5ee7-436c-9ea3-36101c4ed797 · outbound

This paper cites RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem.

How to craft a deep reinforcement learning policy for wind farm flow control RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.360351Z

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 ba181749-c3b1-4858-ba9d-9e86d4bb4bad · outbound

This paper cites Array programming with NumPy.

How to craft a deep reinforcement learning policy for wind farm flow control Array programming with NumPy

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.908444Z

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Unavailable: canonical work link unavailable.

Observation 682d21f7-fc5a-4c3d-9dd7-256a2fd47daf · outbound

This paper cites Ray: A Distributed Framework for Emerging AI Applications.

How to craft a deep reinforcement learning policy for wind farm flow control Ray: A Distributed Framework for Emerging AI Applications

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:54.911065Z

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Unavailable: canonical work link unavailable.

Observation 4b13fdab-e8d8-4f5f-911e-7da90dcf44dc · outbound

This paper cites During training, actions are sampled independently for each turbine as ai t ∼ V(µi, κi), while during evaluation, actions are set deterministically to the mode, ai t = µi.

How to craft a deep reinforcement learning policy for wind farm flow control During training, actions are sampled independently for each turbine as ai t ∼ V(µi, κi), while during evaluation, actions are set deterministically to the mode, ai t = µi

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:02:55.339259Z

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

Observation 229f945a-4ecc-40a0-9553-ba7e65756521 · outbound

This paper cites an unresolved cited work.

How to craft a deep reinforcement learning policy for wind farm flow control Unresolved cited work

Reference 1481

Resolution: verified exact; doi, observed 2026-08-07T06:02:54.965803Z

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 89a552fd-460a-4be0-9cf8-208a026c3d8e · outbound

This paper cites an unresolved cited work.

How to craft a deep reinforcement learning policy for wind farm flow control Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:02:55.349599Z

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.

Pith citing papers

No inbound Pith citation observations are available.