Paper Citation Record · LEDGER

V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2110.14555.

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

pith.paper-citation-record.v1
2110.14555 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:10:22.343287Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:36:56.453527Z

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 68fb79ea-9549-4541-8865-d5c546ea554d · inbound

Minimax-Optimal Multi-Agent Robust Reinforcement Learning cites this paper.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.343287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49e2b052-d71b-420e-8e56-835465a3b089 · inbound

Reinforcement Learning on Dyads to Enhance Medication Adherence cites this paper.

Reinforcement Learning on Dyads to Enhance Medication Adherence V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T01:03:36.664136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9046df84-e3c7-4561-affd-f0112bffafbc · inbound

Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games cites this paper.

Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T20:38:32.459881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c102419-2b9f-48c5-a9c2-a2e015e5063a · inbound

Decentralized Collective World Model for Emergent Communication and Coordination cites this paper.

Decentralized Collective World Model for Emergent Communication and Coordination V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:42:11.474558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3a825803-a28a-442c-b7d2-831213d857ff · inbound

Solving Zero-Sum Convex Markov Games cites this paper.

Solving Zero-Sum Convex Markov Games V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:55.468336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0fba221-0e6e-4b2e-9221-fd8a34d175f5 · inbound

Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback cites this paper.

Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:29.057981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 719380bb-5286-492c-a38f-a8c7cf2f568f · inbound

Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation cites this paper.

Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:00:36.023585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a170204b-593d-43a0-b2c1-2c54ab997d0c · inbound

Sample-efficient inductive matrix completion with noise and inexact side-information cites this paper.

Sample-efficient inductive matrix completion with noise and inexact side-information V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 158

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T14:08:21.046990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bd06b4d4-e721-428a-ba68-b8ceeb5c596d · inbound

Regret Minimization with Adaptive Opponents in Repeated Games cites this paper.

Regret Minimization with Adaptive Opponents in Repeated Games V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:36:56.455831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4e6ec9f4-feba-47dc-90c0-1f99416eb1a2 · inbound

PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance cites this paper.

PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T00:59:12.832865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.