Paper Citation Record · LEDGER

Minimax-Optimal Multi-Agent Robust Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2412.19873.

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

pith.paper-citation-record.v1
2412.19873 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:10:22.389652Z

measured 17 of 17 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 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

17 of 17 outbound references displayed

verified exact0
verified fuzzy2
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 915506fb-b58e-4222-b634-75fc90fcb3ae · outbound

This paper cites Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.356655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f938e5a-86e0-4651-8374-64b4a7743f45 · outbound

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

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Markov games as a framework for multi-age nt reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:10:22.658431Z

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 d2895eaa-edec-4d2a-8aaa-4105d8e89c17 · outbound

This paper cites When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.369597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fc7b9a9-a76c-4ba5-823f-38be90703761 · outbound

This paper cites Robust Markov Decision Processes without Model Estimation.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Robust Markov Decision Processes without Model Estimation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.378171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3ce7b97-a151-420a-ba97-31cf6157b70f · outbound

This paper cites $O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning $O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.381901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2a7760c-9654-4f32-9bd4-e2096752ddfa · outbound

This paper cites SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.386033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ebef0ff-5a5c-4bd3-8399-964dae04b62f · outbound

This paper cites SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.389652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation addabe88-3258-4706-b570-cb1396784e6a · outbound

This paper cites URL https://onlinelibrary.wiley.com/doi/abs/10.1002/j.1538-7305.1952.tb01393.x.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning URL https://onlinelibrary.wiley.com/doi/abs/10.1002/j.1538-7305.1952.tb01393.x

Reference 1952

Resolution: malformed identifier; doi_truncated, observed 2026-08-11T00:10:22.635176Z

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 39163fcf-0cbe-496c-a08c-d6efa405b086 · outbound

This paper cites Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning

Reference 1953

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.365457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02150360-0090-4cf1-981c-c15e25cc39c5 · outbound

This paper cites OpenSpiel: A Framework for Reinforcement Learning in Games.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning OpenSpiel: A Framework for Reinforcement Learning in Games

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.352248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ea42e5f-01e7-4d3b-8e87-e50599d88246 · outbound

This paper cites Feature-Based Q-Learning for Two-Player Stochastic Games.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Feature-Based Q-Learning for Two-Player Stochastic Games

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.338397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddeba19e-c782-45bc-ad07-ea24804a82ec · outbound

This paper cites Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.373621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68fb79ea-9549-4541-8865-d5c546ea554d · outbound

This paper cites V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL.

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 3cbd4675-0fb1-45e1-942f-2c23bf85c093 · outbound

This paper cites DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.321172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 573bbe92-7adc-4381-a30f-f0deaafe9f67 · outbound

This paper cites SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.347791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72b44bf1-66ce-4e3c-8873-a7c3b9a4e011 · outbound

This paper cites Fast bell man updates for robust mdps.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Fast bell man updates for robust mdps

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:10:22.669527Z

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 352ad0fb-5974-488d-9a31-d218b7db4354 · outbound

This paper cites What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.329513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.