Paper Citation Record · LEDGER

Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2102.03479.

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

pith.paper-citation-record.v1
2102.03479 v19

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:14:59.400793Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:04:21.499798Z

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 43533506-2473-480d-8d98-dccc59c9c300 · inbound

AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning cites this paper.

AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T11:14:59.400793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94aa0cb9-be1a-4630-82d0-d7576dcee78c · inbound

SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC cites this paper.

SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T05:18:55.025622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef9aecdf-5592-45d8-b10d-93739a96d916 · inbound

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning cites this paper.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T21:26:24.490884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04f0ea72-e37e-47d8-8637-8462b0ee23e6 · inbound

Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks cites this paper.

Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T20:16:34.295609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2416dc8f-1b34-47c4-863b-59cec1a61a9e · inbound

Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers cites this paper.

Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:20.768390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e19c696-c2af-43c6-bd84-71d64f848b02 · 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 Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T17:57:08.380659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4450c4f5-0d6f-4ae3-9786-d399473c0f29 · inbound

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning cites this paper.

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:41:01.328463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87be9471-c38e-4377-927f-6f74d868d945 · inbound

Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learning cites this paper.

Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learning Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:07:22.491175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d50e6c6-d3db-4d39-a98d-e0802776f926 · inbound

Hierarchical Reinforcement Learning in StarCraft Micromanagement with Influence Maps and Cluster-based Scripts cites this paper.

Hierarchical Reinforcement Learning in StarCraft Micromanagement with Influence Maps and Cluster-based Scripts Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:04:21.501560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.