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Paper Citation Record · LEDGER

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2505.14209.

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

pith.paper-citation-record.v1
2505.14209 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:43.089424Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

53 of 53 outbound references displayed

  • verified exact2
  • verified fuzzy39
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65c8b065-a85e-47ef-b361-1eab9e2c38ec · outbound

This paper cites A review of multi-agent perimeter defense games,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game A review of multi-agent perimeter defense games,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-07T15:42:51.485434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation c4f3cc1e-94f7-441c-97bc-b7f3f4597187 · outbound

This paper cites Local-game decomposition for multiplayer perimeter-defense problem,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Local-game decomposition for multiplayer perimeter-defense problem,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.153700Z digest=sha256:20dc2100935a920c82c5e12f791db68941b3d662979c3c2ef876ece0b4b5a440

Observation fa26c9d4-ef36-4ba6-ba65-b9fb0be63621 · outbound

This paper cites Modeling of target tracking system for homing missiles and air defense systems,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Modeling of target tracking system for homing missiles and air defense systems,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.260792Z digest=sha256:ec72122a2b1e6c910c141f3742fc7f8060bcb399a541af028cdeb550e2cf7dcf

Observation c936c982-f38b-41d7-9c21-454a17f37d97 · outbound

This paper cites Simulation of intelligent unmanned aerial vehicle (UA V) for military surveillance,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Simulation of intelligent unmanned aerial vehicle (UA V) for military surveillance,

Reference 4

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raw_fallback, observed 2026-08-07T15:42:50.873793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.370065Z digest=sha256:5fed0340b9738845f92a0828c1b05035dff80703219214302a9ea333999221dd

Observation 5e3fc813-6565-4816-985c-6b53e6e1b204 · outbound

This paper cites A hierarchical deep reinforcement learning framework for 6-DOF UCA V air-to-air combat,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game A hierarchical deep reinforcement learning framework for 6-DOF UCA V air-to-air combat,

Reference 5

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raw_fallback, observed 2026-08-07T15:42:50.695571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.456607Z digest=sha256:8626d8328aff697ead1d1f89a10e099fbc13cb46e579280e7a9ab1d951291f00

Observation 379d3435-9bb1-42f4-a5da-fcd0ed85f181 · outbound

This paper cites an unresolved cited work.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Unresolved cited work

Reference 6

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unresolved
raw_fallback, observed 2026-08-07T15:42:50.474913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.535022Z digest=sha256:7b2c89fedcad2e6c9942244460165716bf1168075349e2c86d4a277f64053d69

Observation 27fbd88d-1b71-4f12-a266-c61365f76bb3 · outbound

This paper cites Competitive perimeter defense on a line,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Competitive perimeter defense on a line,

Reference 7

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raw_fallback, observed 2026-08-07T15:42:50.313945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.658738Z digest=sha256:48477da45b633eff197cef99ea64d2de28e509b4cc681ae7262e56e1b30755a9

Observation fcc9c6d1-6b0c-492b-b6a4-509d87dc53d1 · outbound

This paper cites Team composition for perimeter defense with patrollers and defenders,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Team composition for perimeter defense with patrollers and defenders,

Reference 8

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verified fuzzy
raw_fallback, observed 2026-08-07T15:42:50.185536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.757099Z digest=sha256:cc253cb0e348f50909cc1e0343982d90073a212367dd2546847defacc4e99da9

Observation 324a1788-5e9d-4905-910d-5038cbf905d9 · outbound

This paper cites Cooperative team strategies for multi-player perimeter-defense games,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Cooperative team strategies for multi-player perimeter-defense games,

Reference 9

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raw_fallback, observed 2026-08-07T15:42:49.902523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:38.828945Z digest=sha256:99f298c876e3c7c21c9b3220b2ee13d6960cb7bec134a5f5afae6bdb9db1f52c

Observation 56ba129d-8a6c-4bf3-acaf-aed8e4d3be83 · outbound

This paper cites Perimeter-defense Game on Arbitrary Convex Shapes.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Perimeter-defense Game on Arbitrary Convex Shapes

Reference 10

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verified exact
local_arxiv, observed 2026-08-07T15:42:43.929369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 05b0481a-b4a2-4c99-949c-d298fa63ecf3 · outbound

This paper cites Multivehicle perimeter defense in conical environments,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Multivehicle perimeter defense in conical environments,

Reference 11

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raw_fallback, observed 2026-08-07T15:42:49.718911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 17863a73-ca1d-4a16-b2a8-60180e8cb232 · outbound

This paper cites Perimeter-defense game between aerial defender and ground intruder,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Perimeter-defense game between aerial defender and ground intruder,

Reference 12

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raw_fallback, observed 2026-08-07T15:42:49.560139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:39.085886Z digest=sha256:57081708df3f84b7cafae8eff4e892ccfad9f0d44dc15a5c1291558a63b08726

Observation 4ae7e59d-1413-43d9-99d5-b5c0d91c514e · outbound

This paper cites Defending a perimeter from a ground intruder using an aerial defender: Theory and practice,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Defending a perimeter from a ground intruder using an aerial defender: Theory and practice,

Reference 13

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raw_fallback, observed 2026-08-07T15:42:49.390587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b018a4b0-ec6a-4571-a27e-469d6fa13937 · outbound

This paper cites Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks

Reference 14

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unresolved
no resolver link, observed 2026-08-07T15:42:39.298718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:39.298718Z digest=sha256:de061e97fc8699935e1b21737b21715881071654b15e68895790420ab616a7b6

Observation 312c2a04-06eb-41d2-893f-e1f11cfbf1c5 · outbound

This paper cites Vision-based Perimeter Defense via Multiview Pose Estimation.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Vision-based Perimeter Defense via Multiview Pose Estimation

Reference 15

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verified exact
local_arxiv, observed 2026-08-07T15:42:43.788711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:39.388461Z digest=sha256:9b653d47705d91f82922855d3b542ae5460bfe05ea42b7f8b40ab155b22e75f7

Observation a8d80f7f-51d0-40be-a679-11380716a7e4 · outbound

This paper cites The role of heterogeneity in autonomous perimeter defense problems,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game The role of heterogeneity in autonomous perimeter defense problems,

Reference 16

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raw_fallback, observed 2026-08-07T15:42:49.225002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:39.473745Z digest=sha256:bdd72f0261e248d700e5614aa5b5498c057f455e3967e1d15058357d84b4d8e5

Observation 58c816ab-d90a-473e-b3fd-b6b1b53401e5 · outbound

This paper cites Intercept angle missile guidance under time vary- ing acceleration bounds,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Intercept angle missile guidance under time vary- ing acceleration bounds,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:39.577566Z digest=sha256:387f6c8aca350c8e10fa094916db21dd2d1d5f5034b8f92e87af8b5c386eddc2

Observation 256f5199-5ffc-4291-854c-a9e0c8da7102 · outbound

This paper cites The effects of different wing configurations on missile aerodynamics,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game The effects of different wing configurations on missile aerodynamics,

Reference 18

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raw_fallback, observed 2026-08-07T15:42:48.818595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:39.677375Z digest=sha256:c82cecb91f8e45d3222912d3c1f19eee8c9a0b38c85626d7b1de6ab36aa0f448

Observation 7e93d0f3-3bc3-4c03-9456-843af168c59d · outbound

This paper cites Dynamic Modeling, Guidance, and Control of Missiles,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Dynamic Modeling, Guidance, and Control of Missiles,

Reference 19

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raw_fallback, observed 2026-08-07T15:42:48.674741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:39.769059Z digest=sha256:f082177b81b1f2d4afdb2a73c1e2a1b9a872164dc527e53b656f166bd4cbfa55

Observation 8311fe39-57fa-4673-9453-f8711b8e3172 · outbound

This paper cites Nonlinear Autopilot for Improving Guidance Performance of Dual-controlled Missiles With Lateral Thrust Regulation,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Nonlinear Autopilot for Improving Guidance Performance of Dual-controlled Missiles With Lateral Thrust Regulation,

Reference 20

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raw_fallback, observed 2026-08-07T15:42:48.423402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:39.858799Z digest=sha256:4f75491912cf3f3872d2e1e18d7b736084f4c62ecda1460b2c838974e855e9be

Observation a50164ba-0f11-41e0-9a38-227bd6b50a5f · outbound

This paper cites Wind compensation framework for unpowered aircraft using online waypoint correction,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Wind compensation framework for unpowered aircraft using online waypoint correction,

Reference 21

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raw_fallback, observed 2026-08-07T15:42:48.215889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 520d503d-b98b-489c-a18a-c3f8e79e196f · outbound

This paper cites Attitude control in ascent phase of missile considering actuator non-linearity and wind disturbance,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Attitude control in ascent phase of missile considering actuator non-linearity and wind disturbance,

Reference 22

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raw_fallback, observed 2026-08-07T15:42:47.976040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 29b6f66e-9ec6-456a-b64e-96871d3824b5 · outbound

This paper cites Safe multi-agent reinforcement learning for multi-robot control,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Safe multi-agent reinforcement learning for multi-robot control,

Reference 23

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raw_fallback, observed 2026-08-07T15:42:47.794218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.128968Z digest=sha256:72b4119755f543d18b4c9bcc7bd6630bb873f9944c5034d7ce2172fb6d16c1b0

Observation 6135d2c2-d6fe-40b0-97d1-dfeba5c7e7ca · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Deep reinforcement learning for autonomous driving: A survey,

Reference 24

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raw_fallback, observed 2026-08-07T15:42:47.508583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.188906Z digest=sha256:ce26e8052e74d265ae4c9d95842fbc3387ce7a2be8674ec1721fe47642f898bf

Observation 10c61f30-828e-42a7-8910-6485512d4f5a · outbound

This paper cites Cooperative control for multi-player pursuit-evasion games with reinforcement learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Cooperative control for multi-player pursuit-evasion games with reinforcement learning,

Reference 25

Resolution
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raw_fallback, observed 2026-08-07T15:42:47.272327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.246739Z digest=sha256:0168619a541f5dc1ef73c1e409df6d6818d73cd4c83857677830cc26dfaca271

Observation e67381ad-a566-489a-847b-f2b864d3b10c · outbound

This paper cites An approach to multi-agent pursuit evasion games using reinforcement learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game An approach to multi-agent pursuit evasion games using reinforcement learning,

Reference 26

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raw_fallback, observed 2026-08-07T15:42:47.146654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.348726Z digest=sha256:b5ab6f7a16215d11c4419a7275b4e8af44a91d98483f6ee1d17afebd827ce153

Observation a5364b30-a2ac-4243-a5b8-9ac7ebc4ec71 · outbound

This paper cites Game of drones: Multi-UA V pursuit-evasion game with online motion planning by deep reinforcement learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Game of drones: Multi-UA V pursuit-evasion game with online motion planning by deep reinforcement learning,

Reference 27

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raw_fallback, observed 2026-08-07T15:42:47.013373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.434474Z digest=sha256:7f33b1e1d169f06733ebdc294ebef3347d87119f1b0508488ff13cab45e018b8

Observation 710c10ea-224c-4660-b95e-4577a399ed4c · outbound

This paper cites Maximum Entropy Heterogeneous-Agent Reinforcement Learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Maximum Entropy Heterogeneous-Agent Reinforcement Learning,

Reference 28

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raw_fallback, observed 2026-08-07T15:42:46.821175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.535742Z digest=sha256:f56cd05dd017a4f05b813a5dca04a188d8ccd5b13c3203b942bb1331e751171a

Observation 7547ce98-5989-403d-b972-0fe3dd10ee7d · outbound

This paper cites Heterogeneous-Agent Reinforcement Learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Heterogeneous-Agent Reinforcement Learning,

Reference 29

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raw_fallback, observed 2026-08-07T15:42:46.657243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.657830Z digest=sha256:7958167501c18b45672b8921cca034be35fbc75f021f09065dc1002fe242ed75

Observation c61f88cb-45c9-4b7e-aa09-88fb133624ab · outbound

This paper cites Mean field multi-agent reinforcement learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Mean field multi-agent reinforcement learning,

Reference 30

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raw_fallback, observed 2026-08-07T15:42:46.461921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.759302Z digest=sha256:275b0e2cf59547b80919e39b73b6651d75b5ebe33520176325191124e5e0a035

Observation 12a531f5-9329-45ee-b401-99861dc44209 · outbound

This paper cites Age of information minimization using multi-agent UA Vs based on AI-enhanced mean field resource allocation,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Age of information minimization using multi-agent UA Vs based on AI-enhanced mean field resource allocation,

Reference 31

Resolution
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raw_fallback, observed 2026-08-07T15:42:46.284738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.864615Z digest=sha256:fbe3384222d127fd30e3abfb12575815b0604f81997aba269603fdbf1a60cafa

Observation bdce975b-b2a2-4c57-a50b-99b896aae4e9 · outbound

This paper cites Joint Resource Allocation for V2X Communications With Multi-Type Mean-Field Reinforcement Learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Joint Resource Allocation for V2X Communications With Multi-Type Mean-Field Reinforcement Learning,

Reference 32

Resolution
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raw_fallback, observed 2026-08-07T15:42:46.114954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:40.970120Z digest=sha256:9cf9bdc488309fbd911e05f0b2a633fd723c6be031671e2d416c7468ee549387

Observation 0afa7f07-8595-4382-8b48-767c432eff80 · outbound

This paper cites Mean Field Deep Reinforcement Learning for Fair and Efficient UA V Control,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Mean Field Deep Reinforcement Learning for Fair and Efficient UA V Control,

Reference 33

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T15:42:43.616797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:41.077375Z digest=sha256:a20ea77210b54e7ef83ab9d27cf30407278d1d4318b9a58c30c995d45e795e3c

Observation dee4f7b5-5477-482f-a1f8-33c7f32a061e · outbound

This paper cites Multi Type Mean Field Reinforcement Learning.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Multi Type Mean Field Reinforcement Learning

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:41.184986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:41.184986Z digest=sha256:de7c656c466cad1525b3d62fc808bd0b9bdf3c971a089897aca93a71bb02924f

Observation 38bc597a-1abb-4b0f-a0f6-ced8a82370af · outbound

This paper cites Hierarchical mean-field deep reinforcement learning for large- scale multiagent systems,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Hierarchical mean-field deep reinforcement learning for large- scale multiagent systems,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:45.922723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:41.261386Z digest=sha256:90be7b4d19b6f7a7928b02eb1641c5b4f825254c7b1a689ba16fa3b5ce07529e

Observation 6f150726-7e75-4fc8-a18b-420724056de4 · outbound

This paper cites Weighted mean-field multi-agent reinforcement learning via reward attribution decomposition,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Weighted mean-field multi-agent reinforcement learning via reward attribution decomposition,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:45.672117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:41.382634Z digest=sha256:5b2cd81b380804876874fb0f0e4a7b83bb370dd726b1286111e4678fd872d83a

Observation 036df322-0de1-497a-bcae-144972e55f63 · outbound

This paper cites Attention Is All You Need,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Attention Is All You Need,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:45.444065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5e413bd5-7749-47ff-be18-b0a785b446e2 · outbound

This paper cites Unsupervised Representation Learning in Deep Reinforcement Learning: A Review.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Unsupervised Representation Learning in Deep Reinforcement Learning: A Review

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:41.674213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:41.674213Z digest=sha256:84f0f42c56e21b6d4e6f7b03b08356bb4b1494575ef272bb96c16d7b9121dd74

Observation 1207fbde-401b-4161-b3c6-8aa73c267d56 · outbound

This paper cites Fraccaro, S.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Fraccaro, S

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:45.225758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:41.807648Z digest=sha256:5da68c6be4105e9eb28f73909893f043fc7c0467040eba19754b98da6d167eaa

Observation 7f3d9612-0999-4416-b083-1f582aa50176 · outbound

This paper cites Ha and J.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Ha and J

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:45.046871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:41.877045Z digest=sha256:4c9e150c3b597ce07c4d18c5d62b86edf052ab34f3071731acdfcf9f8744d7bc

Observation ae5c331c-da92-4ead-86a5-b41221d49ccd · outbound

This paper cites World Models.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game World Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:41.944567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:41.944567Z digest=sha256:fc7e94feb3f81c6376c947a2448a9800083282a8f728d6a3fbb2d8e26ac64fb7

Observation 9e21d576-90cf-466f-965b-7036f8dde1f2 · outbound

This paper cites Plannable Approximations to MDP Homomorphisms: Equivariance under Actions.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Plannable Approximations to MDP Homomorphisms: Equivariance under Actions

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:42.026878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:42.026878Z digest=sha256:aa317f0e38440b758a66b0fbd764c922d33e581af4f79d253f5db6b8c28a28d0

Observation b26677a0-cb7a-4c76-9349-7035b64c0361 · outbound

This paper cites Deep Reinforcement Learning in Large Discrete Action Spaces.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Deep Reinforcement Learning in Large Discrete Action Spaces

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:42.109923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:42.109923Z digest=sha256:326eddbfc3e19d2483169490e3b8d63d96d1bb5cb59188af694889a6da9c2aed

Observation b2926790-48f9-448a-8686-a10be5b23d0c · outbound

This paper cites MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:42.213768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:42.213768Z digest=sha256:d145ad49e1d2fb59badeeec989a96da1780d34852cb5a80f444a4433bace6a92

Observation 8a638511-1288-41c9-8a87-59ef9655ccc4 · outbound

This paper cites Learning action representations for reinforcement learning,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Learning action representations for reinforcement learning,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:44.916663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:42.318289Z digest=sha256:6eb7929ed7d5b800a101a1f717b824c197c13075f44066b83bf767d082cb8996

Observation 9ac4c81d-f50c-45c2-aae7-2f93d65379a9 · outbound

This paper cites The Hungarian method for the assignment problem,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game The Hungarian method for the assignment problem,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:44.721298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:42.407479Z digest=sha256:1f90dbf3e5fc263e43ad68ea91dd753654c51618c6337f7a6f3feb36a08f4a6e

Observation 69449878-9d92-4fc5-bf49-784d27c3f492 · outbound

This paper cites an unresolved cited work.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Unresolved cited work

Reference 47

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T15:42:43.341686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:42.505143Z digest=sha256:694c9b1bd59624091986c54ad7f43ee39e41363c2f6793f6a3651f5f0bb7e41d

Observation 13d15a98-7054-4a45-a24a-ecf168993048 · outbound

This paper cites Robust estimation of a location parameter,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Robust estimation of a location parameter,

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:42.587925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:42.587925Z digest=sha256:ae82d6ce406d7779d58bb2578626ee6c12de0ea1a21bf2449d64a6c3c724852f

Observation c0e6bd86-c020-4fe6-864a-bf25a73feff5 · outbound

This paper cites Continuous control with deep reinforcement learning.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Continuous control with deep reinforcement learning

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:42.682393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:42.682393Z digest=sha256:6c1089a2e65cb0ccc438eeb49342da1451a25383f4baa16896d2f49612cf778b

Observation e8720438-3187-4770-8331-15ad22b8bb0f · outbound

This paper cites Addressing function approxima- tion error in actor-critic methods,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Addressing function approxima- tion error in actor-critic methods,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:44.590441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:42.770074Z digest=sha256:3553ec555772fc17ca9a15bc93c0212dcbf83dda1bcd2e4221d11105a0899f6f

Observation c203d6cb-65ae-4b5e-9703-e641c0e661e3 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive envi- ronments,.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game Multi-agent actor-critic for mixed cooperative-competitive envi- ronments,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:44.443084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:42.844272Z digest=sha256:a68dec2ae33945067f127da279a4cf51a84cadb804b2b6399b0edff3eba0ea2d

Observation a557bfc1-3d13-4617-948b-42165aa6fef2 · outbound

This paper cites degree at the same institution, under the supervision of Prof.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game degree at the same institution, under the supervision of Prof

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:44.130662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:43.089424Z digest=sha256:7476afa4f6de192957873035ceb17df5487af26d771cb57ad90a382c35da2628

Observation 0f8e4b14-4df5-4bdc-88f9-7aabac3966c7 · outbound

This paper cites degree at the same institution, under the supervision of Prof.

Embedded Mean Field Reinforcement Learning for Perimeter-defense Game degree at the same institution, under the supervision of Prof

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:42:44.263995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T15:42:42.956255Z digest=sha256:2dc202bb7b5e870f42f52d2c2b69dc8b3ab4dfb393b09d9147aa8079ecee5dc0

Pith citing papers

No inbound Pith citation observations are available.