Paper Citation Record · LEDGER

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:1908.06758.

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

pith.paper-citation-record.v1
1908.06758 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:05:56.319110Z

measured 29 of 29 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 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

29 of 29 outbound references displayed

verified exact1
verified fuzzy18
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 75822514-1ad5-409a-9682-e54421c15dd4 · outbound

This paper cites A concise introduction to multiagent systems and distributed arti/f_icial intelligence.Synthesis Lectures on Arti/f_icial Intelligence and Machine Learning, 1(1):1–71, 2007.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning A concise introduction to multiagent systems and distributed arti/f_icial intelligence.Synthesis Lectures on Arti/f_icial Intelligence and Machine Learning, 1(1):1–71, 2007

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.676535Z

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.

Observation 860b8e76-a03e-4a3c-a536-9328dd2945b4 · outbound

This paper cites Multiagent systems: A survey from a machine learning perspective.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Multiagent systems: A survey from a machine learning perspective

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.663104Z

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.

Observation 01522112-b185-438f-b35f-e04cf9c63c92 · outbound

This paper cites Multiagent systems: a modern approach to distributed arti/f_icial intelligence.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Multiagent systems: a modern approach to distributed arti/f_icial intelligence

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.651282Z

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.

Observation 28143364-de4e-471b-8e1b-3facf58feae3 · outbound

This paper cites Re- inforcement learning for cooperating and communicating reactive agents in electrical power grids.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Re- inforcement learning for cooperating and communicating reactive agents in electrical power grids

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.638690Z

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.

Observation d9b48a66-3725-4483-9e33-cc912b4d3987 · outbound

This paper cites Game theory and multi-agent reinforcement learning.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Game theory and multi-agent reinforcement learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.626137Z

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.

Observation 3b7fb27f-9dca-4f8c-a32d-14e92f75e31e · outbound

This paper cites Multi-agent reinforcement learning: An overview.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Multi-agent reinforcement learning: An overview

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.614681Z

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.

Observation 13330b69-48be-42fc-bbde-c65c44956eb6 · outbound

This paper cites Reinforcement learn- ing: An introduction.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Reinforcement learn- ing: An introduction

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48b12369-703f-4d31-a9f9-58f55ddc59db · outbound

This paper cites Markov games as a framework for multi- agent reinforcement learning.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Markov games as a framework for multi- agent reinforcement learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.595729Z

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.

Observation 30c51125-48f6-42f8-9ca8-f5a80727d9ff · outbound

This paper cites Nash q-learning for general-sum stochastic games.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Nash q-learning for general-sum stochastic games

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.584260Z

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.

Observation dce179ef-6693-4d4b-bdde-3485763441b9 · outbound

This paper cites Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.246293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb0950f4-b9b9-47cd-b3c2-537247efa11a · outbound

This paper cites Generative adversarial nets.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Generative adversarial nets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.250548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a512184e-d9f4-4ae5-8e27-41695822ae73 · outbound

This paper cites Continual lifelong learning with neural networks: A review.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Continual lifelong learning with neural networks: A review

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.566119Z

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.

Observation 1201e5d3-273e-4cc2-b5ca-4d0bccbe69a8 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.554522Z

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.

Observation c94f8fb8-6780-4a0c-bc11-634adc7ac514 · outbound

This paper cites Partially observable markov decision processes for spoken dialog systems.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Partially observable markov decision processes for spoken dialog systems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.543604Z

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.

Observation 73a311e6-3bc4-40de-8d3e-6d99a0ac366c · outbound

This paper cites Multi-agent reinforcement learning: Independent vs.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Multi-agent reinforcement learning: Independent vs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.532012Z

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.

Observation 9b8427b5-fbb2-43f8-9c94-ddd801035f4f · outbound

This paper cites Q-learning.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Q-learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.270771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d01195-3dfa-4614-97f7-87c9149c18a1 · outbound

This paper cites Op- timal and approximate q-value functions for decentralized pomdps.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Op- timal and approximate q-value functions for decentralized pomdps

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.512053Z

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.

Observation 4104dd0f-4a28-46b4-834c-f082fa1f79db · outbound

This paper cites Continuous control with deep reinforcement learning.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Continuous control with deep reinforcement learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.277833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b9d841f-e227-4eda-bc63-32f656b4c283 · outbound

This paper cites Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Value-Decomposition Networks For Cooperative Multi-Agent Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.281813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1db650e1-fcdc-44df-9d33-ff776325af01 · outbound

This paper cites QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.285822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58ad3c4c-d309-4362-a885-ec3fbd27854d · outbound

This paper cites Counterfactual multi-agent policy gradients.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Counterfactual multi-agent policy gradients

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.498499Z

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.

Observation 88ba8e82-9709-4fce-9514-43af7384a78f · outbound

This paper cites Continual Match Based Training in Pommerman: Technical Report.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Continual Match Based Training in Pommerman: Technical Report

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:05:56.390291Z

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.

Observation b187f6ad-70a9-4960-9cba-4be3b72e6743 · outbound

This paper cites Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.297694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a726443-d11b-40f4-99d2-2f7353077062 · outbound

This paper cites Population Based Training of Neural Networks.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Population Based Training of Neural Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.301394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7ceb260-8f06-42df-8d37-126c442e07f2 · outbound

This paper cites A generalized dynamic programming princi- ple and hamilton-jacobi-bellman equation.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning A generalized dynamic programming princi- ple and hamilton-jacobi-bellman equation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.485220Z

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.

Observation 888616d8-b9da-4e97-84f9-4df4ee1b17cc · outbound

This paper cites Convex optimiza- tion.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Convex optimiza- tion

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.473885Z

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.

Observation 8f86083c-fb7e-49f6-9cb3-30dfeb4c2df3 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Policy gradient methods for reinforcement learning with function approximation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.460617Z

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.

Observation 35bf0017-7a5f-4a1a-aa2e-8d1faf32d5f7 · outbound

This paper cites Wasserstein GAN.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Wasserstein GAN

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:56.315578Z

Source-reported events for the cited work

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Observation c8e541bb-16ba-499f-ba0b-32e3f42d7ee1 · outbound

This paper cites Improved training of wasserstein gans.

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning Improved training of wasserstein gans

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:05:56.447835Z

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.

Pith citing papers

No inbound Pith citation observations are available.