Paper Citation Record · LEDGER

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria

As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2510.18183.

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

pith.paper-citation-record.v1
2510.18183 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:58:33.585535Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T05:35:27.427721Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-21T05:39:40.969302Z

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy0
unresolved37
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1088073e-d548-4f06-9931-19bf43e7120b · outbound

This paper cites Oliehoek.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Oliehoek

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:28.629538Z

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Observation 5feadff1-0f2c-4d4b-b486-682ca9f3edf2 · outbound

This paper cites JAX: composable transfor- mations of Python+NumPy programs, 2018.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria JAX: composable transfor- mations of Python+NumPy programs, 2018

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:28.767199Z

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Observation afa7b3a7-1e18-46e9-84b2-db7d8811c84e · outbound

This paper cites Iterative solution of games by fictitious play.Act.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Iterative solution of games by fictitious play.Act

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:28.896870Z

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Observation 41a573df-1c21-47ed-9bfa-b31e9d07894e · outbound

This paper cites Superhuman ai for heads-up no-limit poker: Libratus beats top profession- als.Science, 359(6374):418–424, 2018.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Superhuman ai for heads-up no-limit poker: Libratus beats top profession- als.Science, 359(6374):418–424, 2018

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.020148Z

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Observation e47adac6-4aeb-4ff0-9528-65e7599374b8 · outbound

This paper cites Superhuman ai for multiplayer poker.Science, 365(6456):885–890, 2019.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Superhuman ai for multiplayer poker.Science, 365(6456):885–890, 2019

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.131292Z

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Observation 53985372-219d-419a-b951-5e50b110b483 · outbound

This paper cites Deep counterfactual regret minimization.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Deep counterfactual regret minimization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.250877Z

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Observation 9c660a0f-b007-44a7-8269-8148aac7de85 · outbound

This paper cites A comprehensive survey of multiagent reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2): 156–172, 2008.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria A comprehensive survey of multiagent reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2): 156–172, 2008

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.365021Z

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Observation ce753de8-fec7-4ee7-bc74-7becb206f8b9 · outbound

This paper cites Fast policy extragradient methods for competitive games with entropy regularization.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Fast policy extragradient methods for competitive games with entropy regularization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.496706Z

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Observation d78ab0f0-543d-4421-ae16-e9282366fa44 · outbound

This paper cites Last-iterate convergence: Zero-sum games and constrained min- max optimization.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Last-iterate convergence: Zero-sum games and constrained min- max optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.658080Z

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Observation 31d7292d-e306-4c18-9ebf-315c6a6270e5 · outbound

This paper cites Training gans with optimism.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Training gans with optimism

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.815653Z

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Observation ff6dd9e0-7c05-4dac-b29c-6515361a597d · outbound

This paper cites Springer, 2003.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Springer, 2003

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:29.952558Z

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Observation 60ef99f7-ca8a-4ed4-b836-bb3902e2d54c · outbound

This paper cites Solving for best responses in extensive-form games using reinforcement learning methods.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Solving for best responses in extensive-form games using reinforcement learning methods

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:30.076837Z

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Observation 4975bc87-7f7f-4e1d-a9fe-a659391fe349 · outbound

This paper cites Deep Reinforcement Learning from Self-Play in Imperfect-Information Games.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:30.196277Z

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Observation 859c70e3-709f-462b-bbd5-7b3fc6db3baf · outbound

This paper cites Learning in perturbed asymmetric games.Games Econ.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Learning in perturbed asymmetric games.Games Econ

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:30.366974Z

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Observation ec4c67cc-b910-4747-878e-d0b27864dccc · outbound

This paper cites Adaptive learning in continuous games: Optimal regret bounds and convergence to nash equilibrium.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Adaptive learning in continuous games: Optimal regret bounds and convergence to nash equilibrium

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:30.507732Z

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Observation 4c26b87a-0a5d-4630-959a-c45f8b8e989b · outbound

This paper cites Extensive games and the problem of information.Contributions to the Theory of Games, 2(28): 193–216, 1953.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Extensive games and the problem of information.Contributions to the Theory of Games, 2(28): 193–216, 1953

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:30.623234Z

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Observation 0bc8ca25-92d9-4bd8-aa23-5ce5a533b33d · outbound

This paper cites A unified game-theoretic approach to multiagent reinforcement learning.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria A unified game-theoretic approach to multiagent reinforcement learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:30.740485Z

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Observation 11abbae1-60db-444a-9c05-9e5f8785eb4a · outbound

This paper cites A class of gap functions for variational inequalities.Mathematical Programming, 64(1):53–79, 1994.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria A class of gap functions for variational inequalities.Mathematical Programming, 64(1):53–79, 1994

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:31.095675Z

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Observation df1daf7d-e64e-4663-b3b4-3e275a278838 · outbound

This paper cites Last-iterate convergence in extensive-form games.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Last-iterate convergence in extensive-form games

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:31.247211Z

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Unavailable: canonical work link unavailable.

Observation 38948cf2-175e-4488-8087-3d45ada886f8 · outbound

This paper cites Sample complexity of asynchronous q-learning: Sharper analysis and variance reduction.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Sample complexity of asynchronous q-learning: Sharper analysis and variance reduction

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:31.407152Z

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Observation 8ed87ff8-aa5a-49a6-b185-c31f55e8f2bf · outbound

This paper cites A survey of nash equilibrium strategy solving based on cfr.Archives of Computational Methods in Engineering, 28(4), 2021.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria A survey of nash equilibrium strategy solving based on cfr.Archives of Computational Methods in Engineering, 28(4), 2021

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:31.542497Z

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Observation c8aff3e5-001b-458d-ae7a-f16c007ace6e · outbound

This paper cites Ozdaglar, Tiancheng Yu, and Kaiqing Zhang.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Ozdaglar, Tiancheng Yu, and Kaiqing Zhang

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:31.642192Z

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Observation bff831ab-f6e3-44af-9079-190a12f4c9c9 · outbound

This paper cites Lanier, Roy Fox, and Pierre Baldi.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Lanier, Roy Fox, and Pierre Baldi

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:31.795089Z

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Observation d6df8c92-e7a7-45c8-a867-62924aee2d43 · outbound

This paper cites ESCHER: eschewing impor- tance sampling in games by computing a history value function to estimate regret.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria ESCHER: eschewing impor- tance sampling in games by computing a history value function to estimate regret

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:31.910301Z

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Observation aa134598-acf0-4880-9fce-e5827b7c4f5b · outbound

This paper cites Wang, Pierre Baldi, Tuomas Sandholm, and Roy Fox.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Wang, Pierre Baldi, Tuomas Sandholm, and Roy Fox

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:32.047562Z

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Observation fbb710e8-16e1-42ab-9edd-21c7129bc98d · outbound

This paper cites McKelvey and Thomas R.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria McKelvey and Thomas R

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:32.194779Z

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Observation 96b8ebf1-617d-4cd4-8ebe-bfd287d45af3 · outbound

This paper cites Ortega, Neil Burch, Thomas W.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Ortega, Neil Burch, Thomas W

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:32.332116Z

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Observation b215b5f6-1445-4ce5-82e8-87c9200e7c63 · outbound

This paper cites Reevaluating Policy Gradient Methods for Imperfect-Information Games.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Reevaluating Policy Gradient Methods for Imperfect-Information Games

Reference 29

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Observation 48308ba7-0f88-4847-861b-4ace5d1a8776 · outbound

This paper cites Proximal Policy Optimization Algorithms.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Proximal Policy Optimization Algorithms

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:32.616577Z

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Observation 2bdaee0d-e23a-4ec4-9d6b-4f6675424cca · outbound

This paper cites If multi-agent learning is the answer, what is the question? Artificial intelligence, 171(7):365–377, 2007.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria If multi-agent learning is the answer, what is the question? Artificial intelligence, 171(7):365–377, 2007

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:32.782011Z

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Observation 1386e903-1dae-4db7-a854-17a227d72b91 · outbound

This paper cites an unresolved cited work.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Unresolved cited work

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:32.896649Z

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Observation 7834897e-f1c0-4fdd-bce2-35353c5d097f · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.Science, 362 (6419):1140–1144, 2018.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.Science, 362 (6419):1140–1144, 2018

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:33.066122Z

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Observation c3a4f671-454f-4744-a567-844d8c41fbf4 · outbound

This paper cites Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, and Christian Kroer.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, and Christian Kroer

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:33.183853Z

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Observation 47174499-4607-4911-b25f-e7df15edd7d8 · outbound

This paper cites DREAM: Deep Regret minimization with Advantage baselines and Model-free learning.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria DREAM: Deep Regret minimization with Advantage baselines and Model-free learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:33.334458Z

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Observation a44373cb-2979-4fab-a3df-2ddd9ad8e83c · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:33.510525Z

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Observation 7f3c8b1f-34cd-47b2-9efa-4e0937f01be4 · outbound

This paper cites Bowling, and Carmelo Piccione.

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria Bowling, and Carmelo Piccione

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:33.585535Z

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Observation ff5553eb-4cf0-4138-917c-f9d3bc75472a · outbound

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

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria OpenSpiel: A Framework for Reinforcement Learning in Games

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T08:58:30.975937Z

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Pith citing papers

Observation 69a90323-31b8-4649-a4df-2ea4fed6bc62 · inbound

Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX cites this paper.

Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-21T05:39:40.970877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.