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Paper Citation Record · LEDGER

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations

As of 9 August 2026, this Paper Citation Record lists 100 of 116 outbound references and 0 inbound Pith citation observations for arXiv:2607.23434.

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

pith.paper-citation-record.v1
2607.23434 v1

Coverage vector

measured 100 of 116 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T22:14:20.499842Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

100 of 116 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved99
  • parse uncertain1
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 49acf4ec-07c2-414c-89b7-af15c8f862cd · outbound

This paper cites Proximal Policy Optimization Algorithms.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Proximal Policy Optimization Algorithms

Reference 1

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Observation 7146915c-26c0-4f5b-8099-a2f19d16ede0 · outbound

This paper cites International Conference on Machine Learning , pages=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International Conference on Machine Learning , pages=

Reference 2

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source=arxiv_source observed=2026-07-30T22:14:20.141587Z digest=sha256:ca699d75e2429bbac87935cfabb104770cd488246f9e891f5a47edeeba580cce

Observation b5620605-15c6-49d5-8439-c0e347f76dbd · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 3

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Observation 29309a8c-b2ad-4f0c-b857-8c9b7144939f · outbound

This paper cites The Twelfth International Conference on Learning Representations , year=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations The Twelfth International Conference on Learning Representations , year=

Reference 4

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Observation 69975c02-9aa0-4d81-8c47-56b6c62fdd25 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 5

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Observation ee7d29b1-3f6c-4c97-97be-204d4d140c9b · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Unresolved cited work

Reference 6

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Observation 3e2e31e5-b8f7-4882-8091-9f4be20fe67a · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Unresolved cited work

Reference 7

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Observation 10e5943a-6a5a-4974-92c6-4e5a1778f9dd · outbound

This paper cites an unresolved cited work.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-07-30T22:14:20.164766Z digest=sha256:e688c0bc56af64dd6e78f40721e2c90fd191f8c7cdc99659f6fee8d7eea19104

Observation 87bbf1fe-e19a-400b-8baf-b3c68ff6abe1 · outbound

This paper cites an unresolved cited work.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Unresolved cited work

Reference 9

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Observation f439e2b8-24c0-40c4-b374-9b20ea275df1 · outbound

This paper cites Proceedings of the Nineteenth International Conference on Machine Learning , pages=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Proceedings of the Nineteenth International Conference on Machine Learning , pages=

Reference 10

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Observation 346c216b-31f6-47e6-a2a7-7eb2c9b2b26f · outbound

This paper cites Manufacturing & Service Operations Management , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Manufacturing & Service Operations Management , volume=

Reference 11

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Observation 46a490f0-5348-49ed-b4e0-fb0964c9a40b · outbound

This paper cites Linear convergence of gradient and proximal-gradient methods under the polyak-.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Linear convergence of gradient and proximal-gradient methods under the polyak-

Reference 12

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Observation 2095cccc-c247-4902-b7cf-dee26c7e87ab · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 13

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Observation 099a401f-8e85-475e-bf87-aa1853f035d3 · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 14

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Observation f6a88a3c-1f71-46b0-b514-7a56c5d93017 · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 15

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Observation 315834f6-04ab-4a58-b957-5147ea57365e · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations MIT Sloan Management Review , year=

Reference 16

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Observation 59b7b025-ac21-4718-96c0-b6cb01183709 · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations , author=

Reference 17

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Observation c6bd9013-2599-4377-ad2d-a6fe7ebe58fd · outbound

This paper cites Annals of Operations Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Annals of Operations Research , volume=

Reference 18

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Observation b7a63a8f-0354-4595-8196-ed3f92a66195 · outbound

This paper cites Manufacturing & Service Operations Management , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Manufacturing & Service Operations Management , volume=

Reference 19

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Observation 32f6203d-4932-4598-8ae2-09498f3b2736 · outbound

This paper cites Online Pricing and Allocation with Demand Learning and Fulfillment Cost.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Online Pricing and Allocation with Demand Learning and Fulfillment Cost

Reference 20

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Observation 71b5794b-e42e-4032-ae5b-c92719e6bc37 · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Expert Systems with Applications , volume=

Reference 21

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Observation f0f52f61-45e3-4c3e-9510-1e8e6a44f77c · outbound

This paper cites Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment

Reference 22

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Observation 20788c48-c27f-4b84-8d47-148df03c7dc9 · outbound

This paper cites Production and Operations Management , pages=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Production and Operations Management , pages=

Reference 23

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Observation 632815a0-9a77-4f08-b1ca-f06f2564ffe4 · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Sensors (Basel, Switzerland) , volume=

Reference 24

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Observation 93a81b12-577c-4334-b780-3ecb9735b379 · outbound

This paper cites Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode

Reference 25

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source=arxiv_source observed=2026-07-30T22:14:20.230883Z digest=sha256:b2d8a0d29a69ae1c61f787191e05a4490bc24a18a3742f869da67077d228fe8c

Observation a49705de-0c2a-4eae-918f-29449d9a3e00 · outbound

This paper cites arXiv preprint arXiv:2504.09831 , year=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations arXiv preprint arXiv:2504.09831 , year=

Reference 26

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Observation 1e9dbf4e-33bb-4670-ab08-cdc56641426a · outbound

This paper cites MARLIM: Multi-Agent Reinforcement Learning for Inventory Management.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations MARLIM: Multi-Agent Reinforcement Learning for Inventory Management

Reference 27

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Observation 31fcaba5-a6c6-4294-9302-d511764d83b7 · outbound

This paper cites 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) , pages=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) , pages=

Reference 28

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Observation aeb5b2ab-1996-472b-9c9f-9f4245309b41 · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 29

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Observation 7eded0b3-e422-4f5f-bb62-458467b9caac · outbound

This paper cites Deep Generative Demand Learning for Newsvendor and Pricing.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Deep Generative Demand Learning for Newsvendor and Pricing

Reference 30

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Observation 5cb2488b-3f92-44b1-b171-fae4afca4176 · outbound

This paper cites Manufacturing & Service Operations Management , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Manufacturing & Service Operations Management , volume=

Reference 31

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Observation 73819c71-fcd5-455a-9810-618f810560f6 · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Advances in Neural Information Processing Systems , volume=

Reference 32

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Observation 09afe809-28a1-4720-99b5-33a2b677bcae · outbound

This paper cites Online Joint Assortment-Inventory Optimization under MNL Choices.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Online Joint Assortment-Inventory Optimization under MNL Choices

Reference 33

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Observation 3c06bcdc-40ed-4ae1-8269-8007572d6f7f · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 34

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Observation 03e21a8e-09e7-4887-b0d5-11904aee07fe · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Operations Research , year=

Reference 35

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source=arxiv_source observed=2026-07-30T22:14:20.267170Z digest=sha256:c720c1f3b0a9d9dfddabe37f66a0cee7d3ced99da7c9421c4fd0433e08ebcb8d

Observation 0cb840ce-f662-4bcd-b882-7729b0754aa6 · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations arXiv preprint arXiv:2501.15338 , year=

Reference 36

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source=arxiv_source observed=2026-07-30T22:14:20.270468Z digest=sha256:51ba06248a32e9a7b9be9aa971fbdcba22d25a466f191e4aa075d5ec6c979bbe

Observation 847763e9-00e0-438c-b84f-210ad82525cc · outbound

This paper cites Dynamic Assortment Selection and Pricing with Censored Preference Feedback.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Dynamic Assortment Selection and Pricing with Censored Preference Feedback

Reference 37

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Observation 7195f0fe-1ac6-45cb-99ae-0ef823daf709 · outbound

This paper cites International Transactions in Operational Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International Transactions in Operational Research , volume=

Reference 38

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Observation 80fdec27-3624-4b78-b2df-fb14348d1bcd · outbound

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Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations IIE Transactions , volume=

Reference 39

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source=arxiv_source observed=2026-07-30T22:14:20.281094Z digest=sha256:7897acee5ef0918ed1017b0cd25022da4333f00d2b372b18c4568efb3cc8f819

Observation 003634be-c890-412a-84da-9a66f8fc3052 · outbound

This paper cites 1998 , publisher=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations 1998 , publisher=

Reference 40

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source=arxiv_source observed=2026-07-30T22:14:20.284932Z digest=sha256:9211456bbb84f7932d7086b339055e65c39b5f99acaa00a0035136178f4684de

Observation 678d1e5a-25a1-4f83-b3c4-173b9a14f6fd · outbound

This paper cites 2020 , publisher=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations 2020 , publisher=

Reference 41

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source=arxiv_source observed=2026-07-30T22:14:20.288779Z digest=sha256:841671ddd85be3a715186a8cd24f17c80bec710bedf0d36e9f0674a12ec6ff0f

Observation 5b9edbab-29d7-420b-8982-2440118dfe52 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Fine-Tuning Language Models from Human Preferences

Reference 42

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source=arxiv_source observed=2026-07-30T22:14:20.292269Z digest=sha256:6ac46830a2b05ec7a41dd53396cc73023523b3b88a19301fa0c66f79f6b26a61

Observation 378ab4ee-0ac8-41c0-88b7-d2f1839afeb3 · outbound

This paper cites Advances in neural information processing systems , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Advances in neural information processing systems , volume=

Reference 43

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source=arxiv_source observed=2026-07-30T22:14:20.296116Z digest=sha256:8b34d6350348a4f7c78df10a080c2349063923bdf00eb1446f37916d841d5f86

Observation ccc46e69-998c-4ae6-ba81-5b37ecccccfa · outbound

This paper cites Advances in neural information processing systems , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Advances in neural information processing systems , volume=

Reference 44

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source=arxiv_source observed=2026-07-30T22:14:20.299489Z digest=sha256:c3e2aae17036f8b6711fd10ae95b477a1d9046bbd9c69c08d9bb248865a1f0a7

Observation 2bec7e89-894a-423a-8671-01e25051921a · outbound

This paper cites Discrete Event Dynamic Systems , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Discrete Event Dynamic Systems , volume=

Reference 45

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source=arxiv_source observed=2026-07-30T22:14:20.303003Z digest=sha256:6e90bcbeae8bfd483369038f47388053df0883f11cd917eac977c4a7ee702f39

Observation 07e2cc4d-3b08-4299-8fae-000dad9be46e · outbound

This paper cites Journal of Artificial Intelligence Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Journal of Artificial Intelligence Research , volume=

Reference 46

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source=arxiv_source observed=2026-07-30T22:14:20.306630Z digest=sha256:60d5111043bcb7274aadd341555ed4a857189b458f3246d8feeeeecfd0d5aacf

Observation a72b08fe-dd63-49f8-b7c3-83cd8885c7d8 · outbound

This paper cites ACM Computing Surveys (CSUR) , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations ACM Computing Surveys (CSUR) , volume=

Reference 47

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source=arxiv_source observed=2026-07-30T22:14:20.310153Z digest=sha256:ceb8382e29a1e8c7298c6bf8a9d653f534c4433cbc4fc84f74e5b97ae0ada3ed

Observation 4aed0284-e9ab-472b-8d4f-b2067344f51f · outbound

This paper cites International Journal of Production Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International Journal of Production Research , volume=

Reference 48

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source=arxiv_source observed=2026-07-30T22:14:20.313661Z digest=sha256:ce33b5c50a62892c76c758aedecb121a57df7fccd81433b048ff1216df0e72d4

Observation 8381dc87-26e0-4fa2-9c60-7a5b40f47d4e · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 49

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source=arxiv_source observed=2026-07-30T22:14:20.317441Z digest=sha256:6ad21a7107f577ed837df035f4ca6165f20ca91829ec7d88a85dcccf4dae4261

Observation 01269394-1dde-49cc-a0d1-098c63ad8148 · outbound

This paper cites Journal of Operations Management , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Journal of Operations Management , volume=

Reference 50

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source=arxiv_source observed=2026-07-30T22:14:20.321158Z digest=sha256:81f2f292229bb5f0d11a4b090a4a5c1745494d33107bdde2fa55b768116f7e59

Observation 57b8259e-6cb7-4199-b602-24579fd0c810 · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 51

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source=arxiv_source observed=2026-07-30T22:14:20.324501Z digest=sha256:d57dabd1436099755ae7736971aed35d05bab786296f1b3f929d547eefba0861

Observation fbbdddb2-201f-4d44-b248-8bdba88c8b59 · outbound

This paper cites International Journal of Logistics: Research and Applications , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International Journal of Logistics: Research and Applications , volume=

Reference 52

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source=arxiv_source observed=2026-07-30T22:14:20.328026Z digest=sha256:0b3be7c1b94fd19a917c5c499a6196741cd1e96206b333b96f2a96014a224577

Observation e1933f5c-ce8e-4b06-89c4-c0c517aa0d78 · outbound

This paper cites Operations Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Operations Research , volume=

Reference 53

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source=arxiv_source observed=2026-07-30T22:14:20.331482Z digest=sha256:4667dee728ac173ae9ae5e40a2194fab166c9f97964b3b6e75176197c7439bda

Observation 9bb1b679-6bc3-488e-80ad-3c208a9cb0ac · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 54

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source=arxiv_source observed=2026-07-30T22:14:20.334938Z digest=sha256:1b469f0c7a4a2df7f23d8f57c1bef8e6f336ed0f151eb112c5a0f5a45c777a5c

Observation b0b8eff9-d966-47dc-9bff-77a1965bf362 · outbound

This paper cites European journal of operational research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations European journal of operational research , volume=

Reference 55

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source=arxiv_source observed=2026-07-30T22:14:20.338727Z digest=sha256:d35c196d52df5dd99177224ce8b1c2dfefcedc4e38df666083f96125bbe5d2c5

Observation 18be5104-3020-46e8-a21a-f13eb54f3d88 · outbound

This paper cites ICML , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations ICML , volume=

Reference 56

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source=arxiv_source observed=2026-07-30T22:14:20.342279Z digest=sha256:770f9ba43a9be378354eb4a6fc19758fb6946ef49f71fb406369463e9b8ed83f

Observation 6a83e311-a3e6-43c7-9ec4-1802ba5623e6 · outbound

This paper cites 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012) , pages=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012) , pages=

Reference 57

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source=arxiv_source observed=2026-07-30T22:14:20.346208Z digest=sha256:acf219ac11b9ca0995bfef18838dddc8b59ab9082d2c219617e0598e33186a6a

Observation 230296b3-addf-43ed-9a5d-4b6a26c54c46 · outbound

This paper cites Neural networks , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Neural networks , volume=

Reference 58

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source=arxiv_source observed=2026-07-30T22:14:20.349764Z digest=sha256:5baee785c01066c58f8fa5fdd5d3c8181bf37fd5cb3849ab4b92821a7e522017

Observation 68805eec-e907-4f7f-bbce-b784166bbaff · outbound

This paper cites Concrete Problems in AI Safety.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Concrete Problems in AI Safety

Reference 59

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source=arxiv_source observed=2026-07-30T22:14:20.353304Z digest=sha256:af3e9aa511289bbe5d2ef37c37c6631418886ac3c2394eacf143ba8ff840130e

Observation 5f82a425-2908-425b-ba3f-24a1471b88fe · outbound

This paper cites Synthese , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Synthese , volume=

Reference 60

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source=arxiv_source observed=2026-07-30T22:14:20.357194Z digest=sha256:cd064109c0549f6d0d4142c6073fc6649fdd1c3988703235cfcbda8cb407877f

Observation 6992b26a-caca-4a1c-8386-e19a990e1c48 · outbound

This paper cites 2021 , publisher=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations 2021 , publisher=

Reference 61

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source=arxiv_source observed=2026-07-30T22:14:20.360655Z digest=sha256:dff71344218f8a664bc75014026432185561fe5379e951ec1b61af1af1e16eb0

Observation 1881106e-8ba1-43cb-bbb6-6912e60fd418 · outbound

This paper cites Dyadic Reinforcement Learning.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Dyadic Reinforcement Learning

Reference 62

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source=arxiv_source observed=2026-07-30T22:14:20.364088Z digest=sha256:18aed21c2020a771eebe2d40f6b902a19bd0c9bc9a6d18784a426b30da794223

Observation d8e280bf-2fa4-4796-a5b8-602cb1648561 · outbound

This paper cites Algorithms , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Algorithms , volume=

Reference 63

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source=arxiv_source observed=2026-07-30T22:14:20.367894Z digest=sha256:a104d9b5598caf76e4e15c7976bb421546fcb16ac009113ba8ffde2c6ad57b6a

Observation edc8256d-89c0-4ffa-b6c5-381ef22585a6 · outbound

This paper cites Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies , volume=

Reference 64

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source=arxiv_source observed=2026-07-30T22:14:20.371330Z digest=sha256:b3f039cad8eb9de75bfbab9bba01ee9f51cab7241e344c7138c8fffe64bd00b1

Observation 86a824d1-58d8-4afd-8e12-c96ee97c91c1 · outbound

This paper cites Handbooks in Operations Research and Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Handbooks in Operations Research and Management Science , volume=

Reference 65

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source=arxiv_source observed=2026-07-30T22:14:20.375253Z digest=sha256:b4ca76ef0d05b9960d4ba116d735967813b52c267a20494b0e97bc7880f0454b

Observation 319c1546-b4ae-4cde-902a-bd9906beb9e1 · outbound

This paper cites The Roots of Logistics , pages=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations The Roots of Logistics , pages=

Reference 66

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source=arxiv_source observed=2026-07-30T22:14:20.379148Z digest=sha256:4cc7eb89fb81e81523a395c45d06b3f5d5d5908c9805014524e116f37f10daff

Observation bb9d71a1-8dd9-4ffd-8ddc-4ade20d03799 · outbound

This paper cites Operations Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Operations Research , volume=

Reference 67

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source=arxiv_source observed=2026-07-30T22:14:20.382655Z digest=sha256:1974996bf29b8022236ee0ab7cb09acd5ce3e664b98ee901b73097ca0f6323bb

Observation 63d1c5cf-c588-4cc1-98db-9f9e8b7f2d5c · outbound

This paper cites International Journal of Production Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International Journal of Production Research , volume=

Reference 68

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source=arxiv_source observed=2026-07-30T22:14:20.386086Z digest=sha256:c5751791c799593dd6c165c93a0efcf55bb5a2d6ed216a0fe98256474718883e

Observation 2c33bf29-0257-4cb0-baae-3d34b0935e79 · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 69

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source=arxiv_source observed=2026-07-30T22:14:20.389520Z digest=sha256:7be71768497c41376a86bfec2cd802bbcad34b462523f3e4e6dc67caf7aa08d9

Observation 2b32069c-0f43-45de-9439-af9e4b2e1776 · outbound

This paper cites MIT Sloan Management Review , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations MIT Sloan Management Review , volume=

Reference 70

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source=arxiv_source observed=2026-07-30T22:14:20.393019Z digest=sha256:d4c68ae95f496063f650908076cb3f78d3b60169c8f1e0f750e638e41d5786dd

Observation 6f66183f-46b8-443a-89b7-43cc72e65861 · outbound

This paper cites Production and Operations Management , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Production and Operations Management , volume=

Reference 71

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source=arxiv_source observed=2026-07-30T22:14:20.396549Z digest=sha256:66b2f5327801f85446a5ee82279ce60e3a5f7f35b5573d2f7a547c8524c536f3

Observation bed2cf27-075f-4776-a8f6-fd9d63977196 · outbound

This paper cites Operations Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Operations Research , volume=

Reference 72

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source=arxiv_source observed=2026-07-30T22:14:20.399873Z digest=sha256:40f7e02bd7c3cd94ac1a59e9297400908ab840a1e6fe90a498ef993f5166000f

Observation 8de6d193-06c5-4fca-a005-8a789a4bb450 · outbound

This paper cites Electronic Commerce Research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Electronic Commerce Research , volume=

Reference 73

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source=arxiv_source observed=2026-07-30T22:14:20.403555Z digest=sha256:092edaeb55d0804cc3097cd5a28325a54a086b05a05984b5486aa84ef720a165

Observation f9d2322b-2787-4d7b-a9ba-45c679936e6a · outbound

This paper cites Transportation Research Part E: Logistics and Transportation Review , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Transportation Research Part E: Logistics and Transportation Review , volume=

Reference 74

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source=arxiv_source observed=2026-07-30T22:14:20.407052Z digest=sha256:d560c1871542ee788361321a71400334322b26ab76eca30af74699e87e252fcd

Observation ce0dc897-290d-4092-8a75-bbf8f9dfc338 · outbound

This paper cites International Journal of Production Economics , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International Journal of Production Economics , volume=

Reference 75

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source=arxiv_source observed=2026-07-30T22:14:20.410633Z digest=sha256:510fc6a9e65efd9d651d8f24dbeef51666267a1aa43aae9b2a617b863ae89b7d

Observation 3d997573-044e-40cb-87d3-37bfd15ea511 · outbound

This paper cites 2025 , publisher=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations 2025 , publisher=

Reference 76

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source=arxiv_source observed=2026-07-30T22:14:20.414466Z digest=sha256:1c3d95998fdcc6a84d5e6d170f21193f235173e04f8ee6329b2b2885c5053124

Observation 6a2a9bdf-8ecb-4040-9fa8-934aa9265979 · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 77

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source=arxiv_source observed=2026-07-30T22:14:20.418069Z digest=sha256:97419182da8b116e31b1e368b2dc375dfa85783bb93f656349cb0c27834bd38c

Observation 0d8274ec-c884-4510-8521-ae29aee70b43 · outbound

This paper cites Management Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Management Science , volume=

Reference 78

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source=arxiv_source observed=2026-07-30T22:14:20.421649Z digest=sha256:99eae2174868c92e1559643c715b69b6c14b6c31e7579788d8d10b99ff50132e

Observation 98b6fbe8-132c-40d2-bd9a-25e8987243a8 · outbound

This paper cites Manufacturing & Service Operations Management , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Manufacturing & Service Operations Management , volume=

Reference 79

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source=arxiv_source observed=2026-07-30T22:14:20.425020Z digest=sha256:765b06b5d675389dc27317ca070d328e579494d5e510151f976692fe3f86ecf6

Observation aade1b80-acca-4340-a150-84850fdab827 · outbound

This paper cites Transportation Science , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Transportation Science , volume=

Reference 80

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source=arxiv_source observed=2026-07-30T22:14:20.428417Z digest=sha256:aeddba899119a2b1343d6cff3265581277d1c9d3067e486fe069ff2867b79f61

Observation 4ce596cd-13cf-4db0-9319-ba9c7f0678a1 · outbound

This paper cites 2014 , publisher=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations 2014 , publisher=

Reference 81

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source=arxiv_source observed=2026-07-30T22:14:20.432306Z digest=sha256:bafc563bd3581aefddf2144efb09a957b965a96c4f7ae6576e3961310b841337

Observation e69902a1-688c-42b2-843a-912a7b56f243 · outbound

This paper cites and Keskin, N.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations and Keskin, N

Reference 82

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source=arxiv_source observed=2026-07-30T22:14:20.435843Z digest=sha256:c10ffc3a823d3b5da1e11d6887333ff01c07c163449096c3e4466f13d423d550

Observation be7e1513-6654-4edd-af58-5d50a2483b99 · outbound

This paper cites and Nayga, Rodolfo M.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations and Nayga, Rodolfo M

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source=arxiv_source observed=2026-07-30T22:14:20.439110Z digest=sha256:d1eb896e651942c0e4ba5407d15a4a684342cb0f8c4effc2b8f7ea1cf5cc9211

Observation a8e3d51d-d135-4883-85ff-7fcf2e272051 · outbound

This paper cites Competitive Pricing for Multiple Market Segments Considering Consumers' Willingness to Pay , journal =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Competitive Pricing for Multiple Market Segments Considering Consumers' Willingness to Pay , journal =

Reference 84

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no resolver link, observed 2026-07-30T22:14:20.442575Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T22:14:20.442575Z digest=sha256:13c3c52c59327fc71cc4e57293f8fadc221715b00ee787482aaba069b6b6bf2b

Observation 1153d8e4-4209-41f9-86f4-f71da833939e · outbound

This paper cites Optimizing Inventory and Pricing for Substitute Products with Soft Supply Constraints , journal =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Optimizing Inventory and Pricing for Substitute Products with Soft Supply Constraints , journal =

Reference 85

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T22:14:20.446247Z digest=sha256:04889b7563bce5387e28158ebb6d9df5a78840580a8b50f57351b1a4dd01fab4

Observation 3034383a-75b9-4896-ac7e-bc2e8452b04f · outbound

This paper cites International Journal of Enterprise Information Systems , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International Journal of Enterprise Information Systems , year =

Reference 86

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source=arxiv_source observed=2026-07-30T22:14:20.449708Z digest=sha256:3b82899d52065b66970849781418ea8eecd60752741950e441149dc822506bcc

Observation 9cf4baa0-2c2d-4a2f-a18e-4d8ebed09f49 · outbound

This paper cites and Phuong, Do Thi Thanh and Tedjakusuma, Adi Prasetyo and Eunike, Ixora Javanisa and Riantama, Dalianus , title =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations and Phuong, Do Thi Thanh and Tedjakusuma, Adi Prasetyo and Eunike, Ixora Javanisa and Riantama, Dalianus , title =

Reference 87

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source=arxiv_source observed=2026-07-30T22:14:20.453567Z digest=sha256:e35a238559706d6c7f8fd5b9d17bc34cd4e44844bd53a2aa48b3806b8baaab48

Observation d15f9442-4d5c-4736-b4d6-058c8bdedbb4 · outbound

This paper cites European Journal of Operational Research , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations European Journal of Operational Research , year =

Reference 88

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source=arxiv_source observed=2026-07-30T22:14:20.457252Z digest=sha256:2fec439ffacd26ca2c377c35c8e906c691570b6e963fe3d30be7e50330e33b8a

Observation b3c7ac6d-7adf-4592-b051-3d572abc8fa3 · outbound

This paper cites Heliyon , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Heliyon , year =

Reference 89

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source=arxiv_source observed=2026-07-30T22:14:20.461148Z digest=sha256:2b5bf3f8831890cc307b229884de48a8b97662065f79f07a08a9a17db0de9aec

Observation 6098ef59-f9a9-4d5d-b0a8-75be819406a1 · outbound

This paper cites Robotics and Computer-Integrated Manufacturing , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Robotics and Computer-Integrated Manufacturing , year =

Reference 90

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source=arxiv_source observed=2026-07-30T22:14:20.464725Z digest=sha256:2e998109b4274bf42776a908a8f496185fe538853026bd49fa4329ee1e79b07d

Observation 1a11721b-1e0f-4ce3-9d94-06ff5d764340 · outbound

This paper cites Robotics and Autonomous Systems , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Robotics and Autonomous Systems , year =

Reference 91

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source=arxiv_source observed=2026-07-30T22:14:20.468126Z digest=sha256:586f5f553249d61d9dfc9c299782bcfd72e5e78b76ef88558646420b04fd4388

Observation e3f49721-29f0-40fe-9c8d-00954177854a · outbound

This paper cites and Dasaklis, Thomas K.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations and Dasaklis, Thomas K

Reference 92

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source=arxiv_source observed=2026-07-30T22:14:20.471586Z digest=sha256:52824936a1f53139975849051d905cc313c2409262e5a70865c92d76e330c849

Observation 8bfd05a7-23b5-476d-a181-8892cc3fec92 · outbound

This paper cites an unresolved cited work.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Unresolved cited work

Reference 93

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source=arxiv_source observed=2026-07-30T22:14:20.474970Z digest=sha256:0753bbfe815935be20c7a2315eef484447468aaff6b08e6e82b7c693a851d169

Observation db0654d3-5f7b-4bc1-a206-eaa5cb870d7c · outbound

This paper cites IISE Transactions , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations IISE Transactions , year =

Reference 94

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source=arxiv_source observed=2026-07-30T22:14:20.478417Z digest=sha256:e20a68baf44ae25e7c598325341831a4306ae6ac5de6b044783bdd40be22feb5

Observation e76f3203-87f9-40a5-908f-0b43a29c86e8 · outbound

This paper cites Annals of Applied Sciences , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Annals of Applied Sciences , year =

Reference 95

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source=arxiv_source observed=2026-07-30T22:14:20.481914Z digest=sha256:35c135ca413686f5b562064a2b55528b088fb0d8b7b1628790bcd9f6f1736259

Observation 715cd986-e175-4e6c-a13f-b3bdf981b938 · outbound

This paper cites Production and Operations Management , year =.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations Production and Operations Management , year =

Reference 96

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source=arxiv_source observed=2026-07-30T22:14:20.485408Z digest=sha256:7fa7cd0e1c8e3ef712d80b99200f3971a85f20756501decf482af13ca46423c2

Observation 98256ab4-d1dc-45f4-8568-a2ba56c70b19 · outbound

This paper cites American Economic Review , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations American Economic Review , volume=

Reference 97

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source=arxiv_source observed=2026-07-30T22:14:20.489154Z digest=sha256:0ebd8e559db928de11d219f16f8c4d2edcd4f3b78ba7794511c295b412bf584e

Observation fc4738df-1f74-47a5-b984-f6219e28f831 · outbound

This paper cites The RAND Journal of Economics , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations The RAND Journal of Economics , volume=

Reference 98

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source=arxiv_source observed=2026-07-30T22:14:20.492570Z digest=sha256:8652339b7d833bc9f2f29e275d959e68b4adecd02e444d457c3ea6ad82b7906a

Observation c78438e2-969e-4acc-b09e-d461303232c1 · outbound

This paper cites International journal of production research , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations International journal of production research , volume=

Reference 99

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source=arxiv_source observed=2026-07-30T22:14:20.496139Z digest=sha256:f8623ea8449d9a3143493a2931577ec757a197d5922f2a1d80fdfd460bf6560f

Observation ea32cef8-608c-4e82-8be6-fa1d94a78b14 · outbound

This paper cites IISE Transactions , volume=.

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations IISE Transactions , volume=

Reference 100

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source=arxiv_source observed=2026-07-30T22:14:20.499842Z digest=sha256:e8f2771cbac1b56916f59ad22ca370d04935bf5754b1e9313ad5a7515f286178

Pith citing papers

No inbound Pith citation observations are available.