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Paper Citation Record · LEDGER

A Survey and Critique of Multiagent Deep Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1810.05587.

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

pith.paper-citation-record.v1
1810.05587 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:54:44.049340Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:30:06.960997Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 755b340c-cea3-4314-a2f4-8f5ee45edd33 · inbound

On Multi-Agent Learning in Team Sports Games cites this paper.

On Multi-Agent Learning in Team Sports Games A Survey and Critique of Multiagent Deep Reinforcement Learning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-25T15:56:01.144517Z

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=2026-05-25T15:51:28.289206Z digest=sha256:a8053ed4ee73373d81eeb3ca16149fc40a4731163e55a49dddc3952b9f771176

Observation 129480d0-e810-4143-adda-f77976f36055 · inbound

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning cites this paper.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning A Survey and Critique of Multiagent Deep Reinforcement Learning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-14T14:54:44.049340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:54:44.049340Z digest=sha256:15b4c2eea33e3f515363399d0b01c3f8ae0478c6a0172112944da1e2fb931446

Observation b37c3a59-fe60-4b66-a441-3bc734508b68 · inbound

Effective Reward Specification in Deep Reinforcement Learning cites this paper.

Effective Reward Specification in Deep Reinforcement Learning A Survey and Critique of Multiagent Deep Reinforcement Learning

Reference 128

Resolution
unresolved
no resolver link, observed 2026-08-11T19:09:54.250875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T19:09:54.250875Z digest=sha256:fdacf6590ad1e2c3e176ca8b7fe073644a8e9823a2675afeec0c92700ad31a46

Observation 2034bc02-3eb1-41b6-9bbd-257144e90cd9 · inbound

Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods cites this paper.

Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods A Survey and Critique of Multiagent Deep Reinforcement Learning

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-04T19:30:06.962489Z

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=2026-06-25T21:21:37.477077Z digest=sha256:047e6c897f6faf9dae7f0362a743729e98730a27c121b8b4021890bae049cb1f

Observation 299cbb76-a9da-4fa8-be74-31bd5556610c · inbound

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details A Survey and Critique of Multiagent Deep Reinforcement Learning

Reference 240

Resolution
unresolved
no resolver link, observed 2026-08-05T15:25:40.447981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T15:25:40.447981Z digest=sha256:55c62d609d44b250840ba886bf2574a67e6572ea04dfa0fb571edb6ed8d1d480