Paper Citation Record · LEDGER

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:1906.04737.

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

pith.paper-citation-record.v1
1906.04737 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:38:36.581037Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T05:26:38.670814Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 950c9c9d-40a0-4b68-a390-3907c494525f · inbound

Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning cites this paper.

Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:36.581037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 757a0885-56dc-49e2-b780-42628ca23c2b · inbound

Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System cites this paper.

Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T15:42:33.266206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b941258a-95ae-4172-a86d-9435420f5286 · inbound

Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage cites this paper.

Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-19T11:12:15.549194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8928dc84-dfe8-4279-a17d-623ef983419a · inbound

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning cites this paper.

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T20:39:08.396975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c582f253-617e-48fb-9a90-ed2562f6f5a2 · inbound

GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective cites this paper.

GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-06T17:57:08.564397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 397a6f5a-5d19-4706-83c2-123c19e6e388 · inbound

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning cites this paper.

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T10:40:38.836637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dac44fd-5103-4128-96ab-8a5ba1832ab6 · inbound

An Improved Multi-Agent Algorithm for Cooperative and Competitive Environments by Identifying and Encouraging Cooperation among Agents cites this paper.

An Improved Multi-Agent Algorithm for Cooperative and Competitive Environments by Identifying and Encouraging Cooperation among Agents Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T19:01:24.672474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4606db2-984e-4b12-8f92-4321f25089d2 · inbound

Virtual Agent Economies cites this paper.

Virtual Agent Economies Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T18:09:37.656673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e5fec68-5039-4239-8f19-c9b33140ac99 · inbound

ORCHID: Fairness-Aware Orchestration in Mission-Critical Air-Ground Integrated Networks cites this paper.

ORCHID: Fairness-Aware Orchestration in Mission-Critical Air-Ground Integrated Networks Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T02:40:00.430146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8dfa37d-8754-48ba-a3f5-a8e1f19ed998 · inbound

RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach cites this paper.

RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-21T09:54:58.227217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e3cefc1-c2ba-4fcf-8788-ef36ba0967b8 · inbound

Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks cites this paper.

Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:55:58.927572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9cee4e3-a59e-489d-bc31-c50375ae4387 · inbound

ERPPO: Entropy Regularization-based Proximal Policy Optimization cites this paper.

ERPPO: Entropy Regularization-based Proximal Policy Optimization Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 64

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T19:32:51.861444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f69b792e-9dc2-40ea-a966-6795dddf6b72 · inbound

Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling cites this paper.

Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:33:16.811585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e530a45-7265-4dcc-8abf-393d477395f0 · inbound

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning cites this paper.

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-25T05:26:38.673729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f8763d9c-0239-4c1e-be38-5e267389d196 · inbound

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning cites this paper.

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-12T13:42:27.758405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73d5b700-0d51-42e6-ab76-52199d439339 · inbound

PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks cites this paper.

PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T16:44:56.264226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.