Paper Citation Record · LEDGER

Concept Learning for Cooperative Multi-Agent Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.20143.

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

pith.paper-citation-record.v1
2507.20143 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:54:40.015064Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

27 of 27 outbound references displayed

verified exact0
verified fuzzy24
unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b0b7e243-d987-42ee-84b5-7f737de4472f · outbound

This paper cites An overview of recent progress in the study of distributed multi-agent coordination,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning An overview of recent progress in the study of distributed multi-agent coordination,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.307534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d206734-9d8c-4906-9dad-af13eb45dccf · outbound

This paper cites Coordinated multi-agent reinforcement learn- ing in networked distributed pomdps,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Coordinated multi-agent reinforcement learn- ing in networked distributed pomdps,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.297615Z

Source-reported events for the cited work

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Observation e426b1ce-340a-4955-9221-bd1e3c7c7d3b · outbound

This paper cites Guided Deep Reinforcement Learning for Swarm Systems.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Guided Deep Reinforcement Learning for Swarm Systems

Reference 3

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Observation f72e07a2-03fc-4349-9736-c9ddc1379ef9 · outbound

This paper cites Value-decomposition networks for cooperative multi-agent learning based on team reward,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Value-decomposition networks for cooperative multi-agent learning based on team reward,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.287321Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7822d528-5e28-46ee-9d20-fa2960101324 · outbound

This paper cites QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.277410Z

Source-reported events for the cited work

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Observation 39336a54-30c1-440f-b3e1-b0dc116cc138 · outbound

This paper cites QPLEX: Duplex dueling multi-agent Q-learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning QPLEX: Duplex dueling multi-agent Q-learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.267340Z

Source-reported events for the cited work

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Observation b1d50fa8-ddb3-42e2-8b90-4bc84676875f · outbound

This paper cites QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.256094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation df7b297f-d76e-4e47-be4b-d7bef7cca0f2 · outbound

This paper cites Q-value path decomposition for deep multiagent reinforcement learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Q-value path decomposition for deep multiagent reinforcement learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.245997Z

Source-reported events for the cited work

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Observation 3bed5604-c962-4c30-a5b4-5258073a274a · outbound

This paper cites Mixrts: Toward interpretable multi-agent reinforcement learning via mixing recurrent soft decision trees,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Mixrts: Toward interpretable multi-agent reinforcement learning via mixing recurrent soft decision trees,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.235540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1db8fb9d-b0ce-41fe-b20c-439c19718111 · outbound

This paper cites Na 2q: Neural attention additive model for interpretable multi-agent q-learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Na 2q: Neural attention additive model for interpretable multi-agent q-learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.225629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8fac033d-5ffd-4307-939b-6bb55547c053 · outbound

This paper cites Shapley Q-value: A local reward approach to solve global reward games,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Shapley Q-value: A local reward approach to solve global reward games,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.214913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 614302bb-e856-4435-a68e-2ae3c35df80e · outbound

This paper cites Graying the black box: Understanding DQNs,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Graying the black box: Understanding DQNs,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.204150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4c8fbfe9-0d29-49b3-8bde-6662685a173b · outbound

This paper cites Interpretation of neural networks is fragile,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Interpretation of neural networks is fragile,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.193665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d30f3fd3-2ade-43aa-8857-79ad4786ae85 · outbound

This paper cites Reliable post hoc explanations: Modeling uncertainty in explainability,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Reliable post hoc explanations: Modeling uncertainty in explainability,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.183522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee9255a8-6f2d-4379-b524-87a3f37c499a · outbound

This paper cites Verifiable reinforcement learning via policy extraction,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Verifiable reinforcement learning via policy extraction,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.173201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4dd55783-bd39-4bcf-9363-5c02edcb9bd1 · outbound

This paper cites Opti- mization methods for interpretable differentiable decision trees applied to reinforcement learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Opti- mization methods for interpretable differentiable decision trees applied to reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.163035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c1a8ad8-da4e-442c-92f1-672c23cc08b2 · outbound

This paper cites Extracting decision tree from trained deep reinforcement learning in traffic signal control,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Extracting decision tree from trained deep reinforcement learning in traffic signal control,

Reference 17

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Source-reported events for the cited work

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Observation e769ae27-4678-47d3-aaa5-7f2779e04d93 · outbound

This paper cites Concept bottleneck models,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Concept bottleneck models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.146569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9a04c297-a971-4450-8056-f0501363b405 · outbound

This paper cites Interactive disentanglement: Learning concepts by interacting with their prototype representations,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Interactive disentanglement: Learning concepts by interacting with their prototype representations,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.136658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8f3b08dd-2aa4-4abd-9ed0-13b9be179d8a · outbound

This paper cites Addressing leakage in concept bottleneck models,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Addressing leakage in concept bottleneck models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.126572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7e2d32cb-712b-4272-a3e9-6934b1a60fdb · outbound

This paper cites Concept gradient: Concept-based interpretation without linear assumption,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Concept gradient: Concept-based interpretation without linear assumption,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.116122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d0f9709c-94e1-4d33-9c03-514984cfb427 · outbound

This paper cites Weighted QMIX: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Weighted QMIX: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.105405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e494a9e0-894a-419b-ae3e-606e644d626c · outbound

This paper cites Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning

Reference 23

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Source-reported events for the cited work

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Observation 1dfd32b4-ed5a-46dc-b530-70f50821749f · outbound

This paper cites Celebrating diversity in shared multi-agent reinforcement learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Celebrating diversity in shared multi-agent reinforcement learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.095200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 173386c7-3fc1-46e7-bf13-29e26066b9f2 · outbound

This paper cites SHAQ: Incorpo- rating shapley value theory into multi-agent Q-learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning SHAQ: Incorpo- rating shapley value theory into multi-agent Q-learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.084972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 203f36bc-1d2f-4038-a0cc-2b34951d3a92 · outbound

This paper cites Shared experience actor- critic for multi-agent reinforcement learning,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning Shared experience actor- critic for multi-agent reinforcement learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.074809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d13a3b0-7274-4d7b-b151-c593e8d22db5 · outbound

This paper cites The StarCraft Multi-Agent Challenge,.

Concept Learning for Cooperative Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:54:40.064822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.