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Paper Citation Record · LEDGER

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design

As of 16 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:1908.06884.

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

pith.paper-citation-record.v1
1908.06884 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:36:33.732226Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

49 of 49 outbound references displayed

  • verified exact1
  • verified fuzzy44
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f62ce19-233e-4c21-965f-8ee508f7eb32 · outbound

This paper cites Zarchan, Tactical and strategic missile guidance.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Zarchan, Tactical and strategic missile guidance

Reference 1

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raw_fallback, observed 2026-08-14T12:36:34.384786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation addb16c3-e75d-4399-8017-db73ae2bce37 · outbound

This paper cites State-space interpolation for a gain-scheduled autopilot,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design State-space interpolation for a gain-scheduled autopilot,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.546303Z digest=sha256:a1739abb06487e3d1c50cf7e17db5387dbf03c59984a61b0a626328b4a1c593d

Observation ec1c530b-9507-4186-b24c-72e6129b2db4 · outbound

This paper cites Interpolation of observer state feed- back controllers for gain scheduling,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Interpolation of observer state feed- back controllers for gain scheduling,

Reference 3

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raw_fallback, observed 2026-08-14T12:36:34.361579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8388f5cb-3caa-472f-a22d-4d0c1b95f39e · outbound

This paper cites Missile autopilot design: gain-scheduling and the gap metric,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Missile autopilot design: gain-scheduling and the gap metric,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.554642Z digest=sha256:9924f0b7fda25f8bd0d2b3c7a74704d954b3749f29cdef041f7a760e5c34e0fc

Observation 42d26474-cacb-4c84-a338-84020363026c · outbound

This paper cites Gain-scheduling control design in the presence of hidden coupling terms,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Gain-scheduling control design in the presence of hidden coupling terms,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.565461Z digest=sha256:f9d779f9bddde8e3b3d43bb1ee34895af5cd68fd617c6990a7deb371e7260458

Observation 05ffb109-c26b-4430-b47f-570a6a0acbfa · outbound

This paper cites A sliding mode missile pitch autopilot synthesis for high angle of attack maneuvering,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design A sliding mode missile pitch autopilot synthesis for high angle of attack maneuvering,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.570903Z digest=sha256:7c51e1cd442872d1ddbc1e728d7f890e8fcf4c97f84618fe5d36dd7bf2eb0243

Observation eef77cab-d422-43b3-b79f-715c4a256b49 · outbound

This paper cites Robust missile autopilot design via high-order sliding mode control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Robust missile autopilot design via high-order sliding mode control,

Reference 7

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raw_fallback, observed 2026-08-14T12:36:34.314304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.575396Z digest=sha256:f1e0b5b152aa9a625cae29b0022c9358587a6137756fb6f28f8412a9276f98e1

Observation 27fbe001-a9ad-44bb-808f-4196e553d848 · outbound

This paper cites Nonlinear autopilot design for an asym- metric missile using robust backstepping control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Nonlinear autopilot design for an asym- metric missile using robust backstepping control,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.579267Z digest=sha256:331434918058583875027c27595bfcf4590b536f5108d73d10c5494b1f5d6ecf

Observation ca5eb230-a822-458c-8f12-adf5d2e212b0 · outbound

This paper cites Adaptive autopilot design for guided munitions,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Adaptive autopilot design for guided munitions,

Reference 9

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raw_fallback, observed 2026-08-14T12:36:34.286833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.583014Z digest=sha256:77f7dfe930aa631e4700a2bccf42f7c8d10e8b8f420d72337e4206bc274c70bd

Observation 5826ae1b-b3d7-46ea-a946-e20c84033187 · outbound

This paper cites L1 adaptive controller for a missile longitudinal autopilot design,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design L1 adaptive controller for a missile longitudinal autopilot design,

Reference 10

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raw_fallback, observed 2026-08-14T12:36:34.275814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.586660Z digest=sha256:6b5eae09bba03ca88e3f16ae5f49940da6c8529f8dde39f8f8ab277f5833ab65

Observation 423b6db0-c745-4831-85bb-8034dca33e7e · outbound

This paper cites Full envelope missile longitudinal autopilot design using the state-dependent riccati equation method,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Full envelope missile longitudinal autopilot design using the state-dependent riccati equation method,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.590428Z digest=sha256:f9cf8c5876d4ff1caab04cda9936b85558b108c46fad345d73fb83cbaf4d0ecf

Observation 4d7cdf2e-9fb3-4f36-a201-bb531f1d158c · outbound

This paper cites SDRE autopilot for dual controlled missiles,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design SDRE autopilot for dual controlled missiles,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.594040Z digest=sha256:042529508448f32539f625770d493b4f49e65c36c3507eedb089efd666cf0f61

Observation 2fc3708c-aac9-4640-9165-0b5652901f8a · outbound

This paper cites Design and flight test of a robust autopilot for the iris- t air-to-air missile,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Design and flight test of a robust autopilot for the iris- t air-to-air missile,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.597705Z digest=sha256:8d8b480cbe585e74be71a3e83cd5e14b7944b00b57b1949a70c93d4d3bad0cb8

Observation 2cbc6570-b896-4519-9d16-3e8623712c50 · outbound

This paper cites Augmented three-loop autopilot structure based on mixed-sensitivity H∞ optimization,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Augmented three-loop autopilot structure based on mixed-sensitivity H∞ optimization,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.601370Z digest=sha256:4c58ee8a81ae7965df158bd0ba314fa3019d51434b1633e5e416f7028766d34c

Observation e515d495-4aae-47af-a308-70fb0d64653c · outbound

This paper cites Connections between linear and nonlinear missile autopilots via three-loop topology,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Connections between linear and nonlinear missile autopilots via three-loop topology,

Reference 15

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.604607Z digest=sha256:8439a827e4f82a6fc9f94fb1760c94d81a68192a68e999593dcbaa31a9a01278

Observation 84a74119-eddf-4c6f-8125-6a7fb2f9a395 · outbound

This paper cites Introducing computational guidance and control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Introducing computational guidance and control,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.607829Z digest=sha256:3c283628186b31f4c44c7383aacf8e0523bb6bcb84ace505ac970f2313afc1c4

Observation dd7d4862-6d9c-49a3-bf3a-f27ba350ee11 · outbound

This paper cites Predictive functional control-based missile autopilot design,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Predictive functional control-based missile autopilot design,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.611056Z digest=sha256:f385e7755d8cf337a32feea4d7adad0c163e74a1e5305152d834a9e3a285e556

Observation cb812eb7-3f17-4699-83a8-94c922f3a5ef · outbound

This paper cites Nonlinear model predictive missile control with a stabilising terminal constraint,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Nonlinear model predictive missile control with a stabilising terminal constraint,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.616233Z digest=sha256:70359536f39b7102d6a3b5034746ad0332cf6fc39c2593086f28d007347b69eb

Observation 19cc51e0-0057-4b89-a269-8b9b8e2897f7 · outbound

This paper cites Nonlinear model-predictive integrated missile control and its multiobjective tuning,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Nonlinear model-predictive integrated missile control and its multiobjective tuning,

Reference 19

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.619837Z digest=sha256:29c5fea2a0e3740de412490db80a4b0f718bb48feb6f7348ba9365b4ce49ba60

Observation 18467c32-4127-4ed7-8cb8-5bc9c49ccec5 · outbound

This paper cites Control system optimization using genetic algorithms,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Control system optimization using genetic algorithms,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.623776Z digest=sha256:f47a13b50b2336a0518f8f0494ca8e97b26d66a587536be6e7fb41c27069ee83

Observation 8dd8d848-f748-4e04-bf3f-7980cf241bcb · outbound

This paper cites Multivariable con- troller design for aircraft longitudinal autopilot based on particle swarm optimization algorithm,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Multivariable con- troller design for aircraft longitudinal autopilot based on particle swarm optimization algorithm,

Reference 21

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.627425Z digest=sha256:752873e066f190c5d36d8084f47d7da7ed5795ec59e457ce955a43dd38710fb8

Observation 1d772b46-205e-4650-af4d-e3e0f640eed9 · outbound

This paper cites Adaptive critic nonlinear robust control: A survey,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Adaptive critic nonlinear robust control: A survey,

Reference 22

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.630974Z digest=sha256:23554af58a5118886cdeb35f466931756084bbec459f04b2270f3b1dbc6355d5

Observation 01656d7e-75c2-49c4-aefe-3afdf403fecc · outbound

This paper cites Transforming cooling optimization for green data center via deep reinforcement learning,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Transforming cooling optimization for green data center via deep reinforcement learning,

Reference 23

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 5701e68e-f82c-4bbb-a5aa-d6fafbbbbc1a · outbound

This paper cites Cooperative deep reinforcement learning for large-scale traffic grid signal control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Cooperative deep reinforcement learning for large-scale traffic grid signal control,

Reference 24

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1c575972-4d1c-441b-8676-9d47c1b6957a · outbound

This paper cites Reinforcement learning for uav attitude control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Reinforcement learning for uav attitude control,

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 976bba5a-8d38-4494-90e5-56cde6928c6b · outbound

This paper cites Parameterized batch reinforcement learning for longitudinal control of autonomous land ve- hicles,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Parameterized batch reinforcement learning for longitudinal control of autonomous land ve- hicles,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.646638Z digest=sha256:7acf0d4b8f6c21e980c556d5c51fece22b0f64f2c5727c9f98817f7a288e76cb

Observation 0b715177-6a05-4d51-bb7d-2c69c0e91dae · outbound

This paper cites Adaptive partial rein- forcement learning neural network-based tracking control for wheeled mobile robotic systems,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Adaptive partial rein- forcement learning neural network-based tracking control for wheeled mobile robotic systems,

Reference 27

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raw_fallback, observed 2026-08-14T12:36:34.075426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.650306Z digest=sha256:4bd0d87ff2829bca1252bd80a28e2679998c224dbbb1e7b5634e8a6ae82f0956

Observation f501f3c2-d9d5-4f50-b8b9-74f39db68b9b · outbound

This paper cites Adaptive neural network control of auvs with control input nonlinearities using reinforcement learning,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Adaptive neural network control of auvs with control input nonlinearities using reinforcement learning,

Reference 28

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raw_fallback, observed 2026-08-14T12:36:34.061658Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation ef77dbcb-87a1-4884-aecc-66dd19452797 · outbound

This paper cites Online adaptive critic flight control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Online adaptive critic flight control,

Reference 29

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raw_fallback, observed 2026-08-14T12:36:34.049422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.658412Z digest=sha256:dbf8008e941451f53fe22a2ab60f375fdaa58b46457207c3ac0f6ad2175df115

Observation 3a590c27-bc25-4699-ba49-fa2599548e66 · outbound

This paper cites Helicopter trimming and tracking control using direct neural dynamic programming,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Helicopter trimming and tracking control using direct neural dynamic programming,

Reference 30

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raw_fallback, observed 2026-08-14T12:36:34.035375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.661935Z digest=sha256:8a77ea8118e50c393d34089743c24338a4fea7c1332a376678cdf6cb9a06c3e9

Observation 9cd2cc7b-d3e5-40d2-b91e-63ce10a548e0 · outbound

This paper cites Incremental model based online dual heuristic programming for nonlinear adaptive control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Incremental model based online dual heuristic programming for nonlinear adaptive control,

Reference 31

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raw_fallback, observed 2026-08-14T12:36:34.022425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.664987Z digest=sha256:bfdda30f12de752d5bd984fab96ecbf541b3d780efb6514ad81572ecc78658fe

Observation 9db8eeac-18a4-4662-93b7-54bd692fcdc1 · outbound

This paper cites Deterministic policy gradient with integral compensator for robust quadrotor control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Deterministic policy gradient with integral compensator for robust quadrotor control,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.668266Z digest=sha256:7c9216a86fd0e7231be643381b28ff3e51c047c071d8ce17556fd2912d9d0602

Observation 9e8bbada-38ff-4a52-8390-33f22389419e · outbound

This paper cites Morphing control of a new bionic morphing uav with deep reinforcement learning,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Morphing control of a new bionic morphing uav with deep reinforcement learning,

Reference 33

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raw_fallback, observed 2026-08-14T12:36:33.994626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.671505Z digest=sha256:206dfb3554ceb7f48be1ed485d1e5d91d04b1b0c6e132942037baad4a94c915a

Observation dac9a0e7-f374-4e59-972b-8739749e7c99 · outbound

This paper cites Depth control of model-free auvs via reinforcement learning,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Depth control of model-free auvs via reinforcement learning,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.983044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.674580Z digest=sha256:490308055a3227379d621c83a2236dffb1fee497d60432884c183a1022d70690

Observation d452e458-de1e-4624-9f76-d4443836cf7b · outbound

This paper cites Using background knowledge to speed reinforcement learning in physical agents,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Using background knowledge to speed reinforcement learning in physical agents,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.970346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.678153Z digest=sha256:118321a602cd4f9ff6c0fff4dc3da16591119baec68e76bf10138daff3ed31a9

Observation 832e853c-a0a4-42c1-bddf-845fc764ba80 · outbound

This paper cites Deep reinforcement learning with prior knowledge,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Deep reinforcement learning with prior knowledge,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.957726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.681822Z digest=sha256:80805c19e083f332bddd1e15cad9d01195f2ef9aefe52a43c8aa6e776d8896a6

Observation 57729dc6-7077-426c-8d3f-37550489b403 · outbound

This paper cites Knowledge matters: Importance of prior information for optimization,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Knowledge matters: Importance of prior information for optimization,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.944036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.685266Z digest=sha256:b7fd5316f9b6343fe56db8f11b5d4a08ef13c3f79a0372f7cfaffddcf126435f

Observation 9fc19499-81a2-44a2-a492-79fa29d79dd8 · outbound

This paper cites The value of prior knowledge in machine learning of complex network systems,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design The value of prior knowledge in machine learning of complex network systems,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.931482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.688957Z digest=sha256:4ffe29aa51b0e63d847bf8732e3708acd5c7ec997b34d91b516777aa0c087d53

Observation bec29fba-c04b-4856-836b-40b048e0f9c4 · outbound

This paper cites Using prior knowledge to improve reinforcement learning in mobile robotics,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Using prior knowledge to improve reinforcement learning in mobile robotics,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.918702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.693005Z digest=sha256:6d1479d858bd1ff1b91c56d33355593889d490a7bb1a6e218d575bdcd87b35f3

Observation a0a881f1-87d6-4aa0-bd63-4d8fbe89a242 · outbound

This paper cites Continuous control with deep reinforcement learning.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Continuous control with deep reinforcement learning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-14T12:36:33.697136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:36:33.697136Z digest=sha256:866c2fe78ba7790989e7a39cdef3ba04c99285ef0b7dcdbcb7a8d31c1fe9d650

Observation 21801b8f-fd30-4d0a-a93b-8b53e001de79 · outbound

This paper cites Low-frequency learning and fast adaptation in model reference adaptive control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Low-frequency learning and fast adaptation in model reference adaptive control,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.905796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.701457Z digest=sha256:56e072c09a462efbecabed7961f922a6c8c4387dab71eec872d9df57640b8831

Observation 1d6f37d3-633e-41bb-869a-c15274b587ee · outbound

This paper cites Connections Between Adaptive Control and Optimization in Machine Learning.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Connections Between Adaptive Control and Optimization in Machine Learning

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-14T12:36:33.804745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.705350Z digest=sha256:ef155b973bd06c951441c2b71d9ffc5131d8b495b151d3d9717a39ce97980023

Observation 45caf91c-5813-4a93-9b51-b9eadb6532ab · outbound

This paper cites Nonsmooth H∞ synthesis,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Nonsmooth H∞ synthesis,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.892435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.709552Z digest=sha256:dba82f003f744c518d6a0d8d84ddfeee17a64130c1ab749680425301f2963859

Observation 4f7e1628-afda-4ac4-9971-941ed797432b · outbound

This paper cites Parametric robust structured control design,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Parametric robust structured control design,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.880237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.713582Z digest=sha256:a1a1347edbf1b6b7bc4e3cc048c51395248e0e899ac0f4b90adb8be3e99a5959

Observation c4ba23eb-f968-4b1d-9280-ca8b63baf995 · outbound

This paper cites Effect of missile config- uration and inertial measurement unit location on autopilot response,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Effect of missile config- uration and inertial measurement unit location on autopilot response,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.866581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.717852Z digest=sha256:f5276a8ecf62aa6d8eaa799966f4af7fec4a75a07dd1ab2b54a508f14a2d1273

Observation 6c00ee64-2257-446d-a81e-7ac64897b577 · outbound

This paper cites Missile longitudinal autopilots: comparison of multiple three loop topologies,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Missile longitudinal autopilots: comparison of multiple three loop topologies,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.853083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.721853Z digest=sha256:dc969a495b9e8783773da612c6594123a0f6573388bf2fb00432e6edf64afad0

Observation 13b0ce03-1e68-458b-beb0-2e34b62efee5 · outbound

This paper cites Bench- marking deep reinforcement learning for continuous control,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Bench- marking deep reinforcement learning for continuous control,

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-14T12:36:33.725105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:36:33.725105Z digest=sha256:b290338dda0bd24b2c82cb974f5641623d1c5c0ad201ac5cf5284c62181ceddc

Observation ecfabc06-e054-4654-9562-43606f7d4c34 · outbound

This paper cites Deep reinforcement learning that matters,.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Deep reinforcement learning that matters,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:36:33.833235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T12:36:33.728824Z digest=sha256:08433f4a18b107ad8d184e6299bb36869a787ba04a0843b3098a2cdea7962ca7

Observation ce9df28e-b96a-4b25-ab38-2443785e4106 · outbound

This paper cites Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control.

A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-14T12:36:33.732226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:36:33.732226Z digest=sha256:60714ffb91d062c8f3d2753f3e6d4062ca40a21b7fa4b795092d6642c6a5c243

Pith citing papers

No inbound Pith citation observations are available.