Paper Citation Record · LEDGER

Mesh-RL: Coupled subgrid reinforcement learning

As of 22 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2606.26333.

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

pith.paper-citation-record.v1
2606.26333 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T01:37:31.834106Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

43 of 43 outbound references displayed

verified exact7
verified fuzzy0
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54b9f506-ecbc-46e7-8545-cbbad43a4436 · outbound

This paper cites Analysis of Temporal-Difference Learning with Function Approximation.Advances in Neural Information Processing Systems, 9, 1996.

Mesh-RL: Coupled subgrid reinforcement learning Analysis of Temporal-Difference Learning with Function Approximation.Advances in Neural Information Processing Systems, 9, 1996

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 48f076ab-4e66-4c6d-851d-1aa57282735c · outbound

This paper cites MIT Press Cambridge, 1998.

Mesh-RL: Coupled subgrid reinforcement learning MIT Press Cambridge, 1998

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation a82dae2d-1272-43de-ab6e-9b75979ff99c · outbound

This paper cites Temporal Difference Learning: Why It Can Be Fast and How It Will Be Faster.

Mesh-RL: Coupled subgrid reinforcement learning Temporal Difference Learning: Why It Can Be Fast and How It Will Be Faster

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation afb1d8ac-6fab-412d-b8f9-9b06225dc913 · outbound

This paper cites Prioritized sweeping: Reinforcement learning with less data and less time.Machine Learning, 13(1):103–130, 1993.

Mesh-RL: Coupled subgrid reinforcement learning Prioritized sweeping: Reinforcement learning with less data and less time.Machine Learning, 13(1):103–130, 1993

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 900da209-a47b-40bb-9e4a-4dde7ae59b28 · outbound

This paper cites Reinforcement Learning with Hierarchies of Machines.Ad- vances in Neural Information Processing Systems, 10, 1997.

Mesh-RL: Coupled subgrid reinforcement learning Reinforcement Learning with Hierarchies of Machines.Ad- vances in Neural Information Processing Systems, 10, 1997

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 5b49c0dd-589b-4188-af65-b29e7c4b6528 · outbound

This paper cites Recent Advances in Hierarchical Reinforcement Learning.Discrete Event Dynamic Systems, 13(4):341–379, 2003.

Mesh-RL: Coupled subgrid reinforcement learning Recent Advances in Hierarchical Reinforcement Learning.Discrete Event Dynamic Systems, 13(4):341–379, 2003

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation d551474c-9a77-4e48-b151-d9779eeede53 · outbound

This paper cites Hierarchical Rein- forcement Learning: A Comprehensive Survey.ACM Computing Surveys (CSUR), 54(5):1–35, 2021.

Mesh-RL: Coupled subgrid reinforcement learning Hierarchical Rein- forcement Learning: A Comprehensive Survey.ACM Computing Surveys (CSUR), 54(5):1–35, 2021

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 0c356e5a-15f1-45b8-8ce2-d1cb3e97768e · outbound

This paper cites Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning.

Mesh-RL: Coupled subgrid reinforcement learning Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:29:55.593473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 345b09b8-a4db-4910-9d1d-18c2b8b77a20 · outbound

This paper cites Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition.Journal of Artificial Intelligence Research, 13:227–303, 2000.

Mesh-RL: Coupled subgrid reinforcement learning Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition.Journal of Artificial Intelligence Research, 13:227–303, 2000

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 9a6f6522-b8fd-4ae7-b45c-17eecba8702a · outbound

This paper cites Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.Artificial Intelligence, 112(1- 2):181–211, 1999.

Mesh-RL: Coupled subgrid reinforcement learning Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.Artificial Intelligence, 112(1- 2):181–211, 1999

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation d5f27410-837b-4de6-a4e5-657166ec40a4 · outbound

This paper cites Discovery of options via meta-learned subgoals.

Mesh-RL: Coupled subgrid reinforcement learning Discovery of options via meta-learned subgoals

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 30553a57-7f88-4db8-b5ec-d7fa2142b581 · outbound

This paper cites Multi-layer abstraction for nested generation of options (mango) in hierarchical reinforcement learning.IFAC-PapersOnLine, 59(26):25–30, 2025.

Mesh-RL: Coupled subgrid reinforcement learning Multi-layer abstraction for nested generation of options (mango) in hierarchical reinforcement learning.IFAC-PapersOnLine, 59(26):25–30, 2025

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 99052512-c3de-4938-9a2e-7ec227edbfc0 · outbound

This paper cites Value Iteration Networks.Advances in Neural Information Processing Systems, 29, 2016.

Mesh-RL: Coupled subgrid reinforcement learning Value Iteration Networks.Advances in Neural Information Processing Systems, 29, 2016

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation be005e62-57fc-44ea-937a-069109442a0f · outbound

This paper cites RUDDER: Return Decomposition for Delayed Rewards.

Mesh-RL: Coupled subgrid reinforcement learning RUDDER: Return Decomposition for Delayed Rewards

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation ffd45b6f-473f-40e7-ab82-39116ebb70a9 · outbound

This paper cites Successor Features for Transfer in Reinforcement Learning.Advances in Neural Information Processing Systems, 30, 2017.

Mesh-RL: Coupled subgrid reinforcement learning Successor Features for Transfer in Reinforcement Learning.Advances in Neural Information Processing Systems, 30, 2017

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 16eaac72-f277-4e59-8d08-5323c324d8dd · outbound

This paper cites The Option-Critic Architecture.

Mesh-RL: Coupled subgrid reinforcement learning The Option-Critic Architecture

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 8b571451-306a-446d-9365-0c52851e27b2 · outbound

This paper cites Go-Explore: a New Approach for Hard-Exploration Problems.

Mesh-RL: Coupled subgrid reinforcement learning Go-Explore: a New Approach for Hard-Exploration Problems

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:29:55.590065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bf6f3d2d-b261-4cc2-a726-c777786dc380 · outbound

This paper cites Springer Science & Business Media, 2004.

Mesh-RL: Coupled subgrid reinforcement learning Springer Science & Business Media, 2004

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 8eb4e794-f4a0-429d-8fe4-1dc3a84807b2 · outbound

This paper cites Butterworth- Heinemann Oxford, UK:, 2013.

Mesh-RL: Coupled subgrid reinforcement learning Butterworth- Heinemann Oxford, UK:, 2013

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 4a734384-1404-4f7f-8e03-ae4f26cc5bcd · outbound

This paper cites Divide-and-Conquer Reinforcement Learning.

Mesh-RL: Coupled subgrid reinforcement learning Divide-and-Conquer Reinforcement Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-04T15:29:55.611156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 49bb79e9-d448-47ba-baee-873586f538a7 · outbound

This paper cites State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning.

Mesh-RL: Coupled subgrid reinforcement learning State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-07-04T15:29:55.600110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 773f1a03-ed0c-4f0a-a1b0-74a8228d4535 · outbound

This paper cites State-space decomposition for reinforcement learning.

Mesh-RL: Coupled subgrid reinforcement learning State-space decomposition for reinforcement learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation c6a8db91-da8a-4640-bb35-faefc48b33ae · outbound

This paper cites Q-Cut—Dynamic Discovery of Sub-goals in Reinforcement Learning.

Mesh-RL: Coupled subgrid reinforcement learning Q-Cut—Dynamic Discovery of Sub-goals in Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation c3341425-e41c-470f-8272-27c169bdee05 · outbound

This paper cites On the bottleneck concept for options discovery: Theoretical underpinnings and extension in continuous state spaces.Masters thesis, McGill University, 2014.

Mesh-RL: Coupled subgrid reinforcement learning On the bottleneck concept for options discovery: Theoretical underpinnings and extension in continuous state spaces.Masters thesis, McGill University, 2014

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 872c1262-a9a4-4202-81ec-44a7c6d5e320 · outbound

This paper cites First results with Dyna, an integrated architecture for learning, planning and reacting.Neural Networks for Control, 179, 1990.

Mesh-RL: Coupled subgrid reinforcement learning First results with Dyna, an integrated architecture for learning, planning and reacting.Neural Networks for Control, 179, 1990

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 357537c1-21e5-468c-be37-2aba8622631b · outbound

This paper cites Proto-Value Functions: Developmental Reinforcement Learning.

Mesh-RL: Coupled subgrid reinforcement learning Proto-Value Functions: Developmental Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 73a004f0-7298-444f-9874-988d478aef5b · outbound

This paper cites Policy invariance under reward transforma- tions: Theory and application to reward shaping.

Mesh-RL: Coupled subgrid reinforcement learning Policy invariance under reward transforma- tions: Theory and application to reward shaping

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 824aeed7-4275-48ab-ba4a-539b19db6c86 · outbound

This paper cites Dealing with Sparse Rewards in Reinforcement Learning.

Mesh-RL: Coupled subgrid reinforcement learning Dealing with Sparse Rewards in Reinforcement Learning

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:29:55.596964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d2cfa473-73e4-4de3-a78f-5961b0f9d0b6 · outbound

This paper cites Reinforcement Learning for Adaptive Mesh Refinement.

Mesh-RL: Coupled subgrid reinforcement learning Reinforcement Learning for Adaptive Mesh Refinement

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 1c51f50c-b37c-4298-aaaf-7c07320d922f · outbound

This paper cites Swarm Reinforcement Learning For Adaptive Mesh Refinement.Advances in Neural Information Processing Systems, 36:73312–73347, 2023.

Mesh-RL: Coupled subgrid reinforcement learning Swarm Reinforcement Learning For Adaptive Mesh Refinement.Advances in Neural Information Processing Systems, 36:73312–73347, 2023

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 248676cc-8692-4bbd-964a-bb6856076830 · outbound

This paper cites Multi-agent reinforcement learning for subgrid-scale modeling of environmental turbulence.

Mesh-RL: Coupled subgrid reinforcement learning Multi-agent reinforcement learning for subgrid-scale modeling of environmental turbulence

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 27689123-81d4-41d0-bbc9-854d160a8d71 · outbound

This paper cites Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagation.

Mesh-RL: Coupled subgrid reinforcement learning Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagation

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:29:55.603573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9623f2d7-51a9-4ff4-bf96-24638003e6ad · outbound

This paper cites arXiv preprint arXiv:2505.16761 , year=.

Mesh-RL: Coupled subgrid reinforcement learning arXiv preprint arXiv:2505.16761 , year=

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:29:55.607579Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4037c0f8-dd2d-43ca-bd1b-07136f5e5bc6 · outbound

This paper cites Courier Corporation, 2003.

Mesh-RL: Coupled subgrid reinforcement learning Courier Corporation, 2003

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 426a2164-7a8c-46f9-b1f0-bad548f73eda · outbound

This paper cites Klaus-Jurgen Bathe, 2006.

Mesh-RL: Coupled subgrid reinforcement learning Klaus-Jurgen Bathe, 2006

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation f4fd38ef-5c62-4f5d-9d2f-e10355b8ddc2 · outbound

This paper cites McGraw- Hill New York, 2005.

Mesh-RL: Coupled subgrid reinforcement learning McGraw- Hill New York, 2005

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 83e426a0-3acc-473d-85b3-1997bace5341 · outbound

This paper cites Parallel domain decomposition software.

Mesh-RL: Coupled subgrid reinforcement learning Parallel domain decomposition software

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 101efb36-fe1c-4d2d-aeac-e6079b93bfe6 · outbound

This paper cites Learning from delayed rewards.Ph.

Mesh-RL: Coupled subgrid reinforcement learning Learning from delayed rewards.Ph

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 63541c50-22f0-411e-8a23-ee117f83d720 · outbound

This paper cites Q-learning.Machine Learning, 8(3):279–292, 1992.

Mesh-RL: Coupled subgrid reinforcement learning Q-learning.Machine Learning, 8(3):279–292, 1992

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 6cb00865-9d9c-4a6a-9522-21a15d0a9e84 · outbound

This paper cites University of Cambridge, Department of Engineering, Cambridge, UK, 1994.

Mesh-RL: Coupled subgrid reinforcement learning University of Cambridge, Department of Engineering, Cambridge, UK, 1994

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation 239cb133-139b-416d-806d-0831f2a3cc4b · outbound

This paper cites Integrated Modeling and Control Based on Reinforcement Learning and Dynamic Programming.Advances in Neural Information Processing Systems, 3, 1990.

Mesh-RL: Coupled subgrid reinforcement learning Integrated Modeling and Control Based on Reinforcement Learning and Dynamic Programming.Advances in Neural Information Processing Systems, 3, 1990

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation ffcd635e-24b5-4aec-9c19-407e0ae8ac17 · outbound

This paper cites Schwarz methods over the course of time.Electron.

Mesh-RL: Coupled subgrid reinforcement learning Schwarz methods over the course of time.Electron

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Observation aa8c2f5e-83d2-4266-a3ac-5391c4132da0 · outbound

This paper cites Generalization in Reinforcement Learning: Safely Approxi- mating the Value Function.Advances in Neural Information Processing Systems, 7, 1994.

Mesh-RL: Coupled subgrid reinforcement learning Generalization in Reinforcement Learning: Safely Approxi- mating the Value Function.Advances in Neural Information Processing Systems, 7, 1994

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-26T01:37:31.834106Z

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Pith citing papers

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