Paper Citation Record · LEDGER

Multi-agent imitation learning with function approximation: Linear Markov games and beyond

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 2 inbound Pith citation observations for arXiv:2602.22810.

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

pith.paper-citation-record.v1
2602.22810 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:42:08.075493Z

measured 38 of 38 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T22:39:42.446147Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T22:43:51.665870Z

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy0
unresolved35
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 306e7af0-72e1-4ff5-b437-219d2c03fcf3 · outbound

This paper cites Definition D.3.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Definition D.3

Reference 1

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Observation 6139846d-4f6c-4695-b8eb-69d7aff91203 · outbound

This paper cites At this point, we can upper bound the sum of the expected local Hellinger divergences with the divergence between trajectories invoking Rohatgi et al.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond At this point, we can upper bound the sum of the expected local Hellinger divergences with the divergence between trajectories invoking Rohatgi et al

Reference 2

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Observation f112b81d-5aca-4ad8-b6ae-0b70d2fdff05 · outbound

This paper cites The most important change is a change of notation.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond The most important change is a change of notation

Reference 3

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Observation a9f86ca2-7b13-4d2e-be84-567a85727555 · outbound

This paper cites Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning

Reference 5

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Observation a12faa65-4d4e-4cac-a40f-47956cbd0c06 · outbound

This paper cites Freihaut, L.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Freihaut, L

Reference 6

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Observation a7e512d2-a188-4a71-9924-2fd5802a6873 · outbound

This paper cites Therefore, Π n softlin is richer and more likely to realize the observe expert behaviour for largeη.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Therefore, Π n softlin is richer and more likely to realize the observe expert behaviour for largeη

Reference 7

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Observation 28bbf6f4-ac7b-44fb-acb9-9b32c1e11c82 · outbound

This paper cites Squeeze-and-Excitation Networks.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Squeeze-and-Excitation Networks

Reference 8

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Observation faebd7c4-9768-43bd-88ee-5c10adc41c4f · outbound

This paper cites Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification

Reference 13

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Observation f613a5c1-9d41-4cef-bbb5-1ff473aa9e06 · outbound

This paper cites PettingZoo: Gym for Multi-Agent Reinforcement Learning.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond PettingZoo: Gym for Multi-Agent Reinforcement Learning

Reference 16

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Observation bfc5417b-4b1e-42c5-a001-a907e3b2406b · outbound

This paper cites IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic

Reference 17

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Observation 71452cbf-5e6d-4c22-ae29-9827f29452c3 · outbound

This paper cites On Reward-Free Reinforcement Learning with Linear Function Approximation.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond On Reward-Free Reinforcement Learning with Linear Function Approximation

Reference 18

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Observation 917d3c25-81d3-43ce-b267-189440240415 · outbound

This paper cites Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

Reference 19

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Observation 19966ab9-24db-4825-b313-ca5a81d7e1cf · outbound

This paper cites 18 Contents of Appendix This appendix provides supplementary material to support the main findings of the paper.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond 18 Contents of Appendix This appendix provides supplementary material to support the main findings of the paper

Reference 20

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Observation 5f52b084-d626-4c79-8e97-d5d950db7759 · outbound

This paper cites an unresolved cited work.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Unresolved cited work

Reference 21

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Observation 83af945a-d3fb-4d71-9623-b96dfd947504 · outbound

This paper cites Reward-free reinforcement learning.Reward free reinforcement learning was first introduced in the seminal work of Jin et al.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Reward-free reinforcement learning.Reward free reinforcement learning was first introduced in the seminal work of Jin et al

Reference 23

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Observation fbf1b2d2-c7bf-45ab-bcb4-8783827f3456 · outbound

This paper cites In these situations, our algorithms could still be applied but the theoretical guarantees would not hold.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond In these situations, our algorithms could still be applied but the theoretical guarantees would not hold

Reference 24

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Observation e0538c15-9123-4110-b35e-47b851be0256 · outbound

This paper cites As a practical example of a Nash equilibrium we can recover consider the zero sum normal form games with payoff matrix 1 0 1 0.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond As a practical example of a Nash equilibrium we can recover consider the zero sum normal form games with payoff matrix 1 0 1 0

Reference 27

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Observation 4106d615-f87d-48e6-852e-6c6f1f75b520 · outbound

This paper cites We notice that Lemma 4.3 avoids completely the dependence onC φ,max.Instead it is replaced with maxπ−n∈Π−n φ πn E ,π−n, h (Λ−n,K h )−1.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond We notice that Lemma 4.3 avoids completely the dependence onC φ,max.Instead it is replaced with maxπ−n∈Π−n φ πn E ,π−n, h (Λ−n,K h )−1

Reference 29

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Observation f271b5be-e7ba-4ea2-b0c4-787d881688a6 · outbound

This paper cites Note that multiple Nash equilibria exist.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Note that multiple Nash equilibria exist

Reference 31

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Observation b6c2e5ed-b0ce-41b4-9207-76ee85c00c36 · outbound

This paper cites The action spaces are discrete, consisting of 9 actions for Tic-Tac-Toe (corresponding to the grid cells) and 7 actions for Connect4 (corresponding to the columns).

Multi-agent imitation learning with function approximation: Linear Markov games and beyond The action spaces are discrete, consisting of 9 actions for Tic-Tac-Toe (corresponding to the grid cells) and 7 actions for Connect4 (corresponding to the columns)

Reference 33

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Observation 15548f71-6475-45a1-84d5-8ab8edda986c · outbound

This paper cites − 1 2 KX i=1 log πE(AE i |Xi) ˆπϵ(AE i |Xi) −log|C ϵ(log Πsoftlin)| − KX i=1 logE.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond − 1 2 KX i=1 log πE(AE i |Xi) ˆπϵ(AE i |Xi) −log|C ϵ(log Πsoftlin)| − KX i=1 logE

Reference 35

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Observation 6990b5dd-13f8-467c-bbe8-b2cb01150a2f · outbound

This paper cites [2025, Lemma F.4] withη= 1, we obtain that KX i=1 E " f πE(AE i |Xi) ¯π(AE i |Xi) 2 F E i # ≤4(2 + logB ratio) KX i=1 E f πE(AE i |Xi) ¯π(AE i |Xi) F E i.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond [2025, Lemma F.4] withη= 1, we obtain that KX i=1 E " f πE(AE i |Xi) ¯π(AE i |Xi) 2 F E i # ≤4(2 + logB ratio) KX i=1 E f πE(AE i |Xi) ¯π(AE i |Xi) F E i

Reference 36

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Observation 61bd9d78-5ca4-488a-b3ca-4cc20d1029f0 · outbound

This paper cites This model is parameterized as a multi-layer feedforward network designed to improve representation learning while maintaining linear transformations.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond This model is parameterized as a multi-layer feedforward network designed to improve representation learning while maintaining linear transformations

Reference 80

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Observation c5f6bb20-8236-4fa5-8514-f3d53446e73e · outbound

This paper cites To address this, we employ an advanced neural architecture inspired by AlphaGo [Silver et al., 2017].

Multi-agent imitation learning with function approximation: Linear Markov games and beyond To address this, we employ an advanced neural architecture inspired by AlphaGo [Silver et al., 2017]

Reference 100

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Observation d36f2df2-e0c0-44db-bfac-490408f44d61 · outbound

This paper cites URLhttps://doi.org/10.1137/1031049.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond URLhttps://doi.org/10.1137/1031049

Reference 1989

Resolution: verified exact; doi, observed 2026-08-02T20:44:03.520469Z

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Observation 959a020e-be39-44fd-93ce-cee6cf3d4b9b · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Playing Atari with Deep Reinforcement Learning

Reference 2013

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Observation 61cef2af-21a5-4853-b3e9-db6e88fb808d · outbound

This paper cites an unresolved cited work.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Unresolved cited work

Reference 2016

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Observation 349ee6f0-86de-41a1-8875-d019a4a94633 · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 2017

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Observation 8a5ebcc5-c199-4bc4-ba0d-29e40dd1504e · outbound

This paper cites an unresolved cited work.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Unresolved cited work

Reference 2018

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Observation 7f64e07a-d333-42a9-9f78-c10be0d1ddcd · outbound

This paper cites Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret

Reference 2019

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Observation 81fd62dc-2065-4699-8421-6631877d0a90 · outbound

This paper cites Fast active learning for pure exploration in reinforcement learning.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Fast active learning for pure exploration in reinforcement learning

Reference 2020

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Observation 7fedc25d-f2de-4fee-9695-5b4b54640aa8 · outbound

This paper cites Towards General Function Approximation in Zero-Sum Markov Games.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Towards General Function Approximation in Zero-Sum Markov Games

Reference 2021

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Observation fb5ad161-668f-40cd-9448-7f49d8601b32 · outbound

This paper cites URLhttp: //dx.doi.org/10.1109/PDGC56933.2022.10053317.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond URLhttp: //dx.doi.org/10.1109/PDGC56933.2022.10053317

Reference 2022

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Observation e1c41c64-d772-4caf-bc1c-0b5120b640ff · outbound

This paper cites Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation

Reference 2023

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Observation eaf52a1c-f9c3-404d-8e13-bf02df8be244 · outbound

This paper cites MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrations.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrations

Reference 2024

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Observation b37fd57c-8eb6-4cdc-b6d7-bd3b39e2a597 · outbound

This paper cites Strongly Solving $7 \times 6$ Connect-Four on Consumer Grade Hardware.

Multi-agent imitation learning with function approximation: Linear Markov games and beyond Strongly Solving $7 \times 6$ Connect-Four on Consumer Grade Hardware

Reference 2025

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Pith citing papers

Observation f6181ae6-7416-488e-a7c0-59ea445f0b76 · inbound

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation cites this paper.

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation Multi-agent imitation learning with function approximation: Linear Markov games and beyond

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-24T02:14:24.653602Z

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Observation 66dc5fdb-0b32-4f84-ae51-67ec2d771ccc · inbound

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation cites this paper.

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation Multi-agent imitation learning with function approximation: Linear Markov games and beyond

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-24T02:14:24.653602Z

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