Paper Citation Record · LEDGER

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation

As of 20 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2509.20008.

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

pith.paper-citation-record.v1
2509.20008 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:17:41.302535Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:15:10.107091Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

60 of 60 outbound references displayed

verified exact1
verified fuzzy0
unresolved59
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c03587f-cd62-46d2-b440-85b222ccc9ea · outbound

This paper cites Agarwal, M.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Agarwal, M

Reference 1

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Observation 49bedf3e-1cba-41db-b6f0-7d884320023a · outbound

This paper cites Akiba, S.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Akiba, S

Reference 2

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Observation a21679c6-6be6-420d-9162-78096056f9a7 · outbound

This paper cites Avalos, F.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Avalos, F

Reference 3

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Observation e296691a-907d-41f6-a834-d20a92fae4d8 · outbound

This paper cites Algorithmsforhyper-parameter optimization.Advances in neural information processing systems, 24, 2011.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Algorithmsforhyper-parameter optimization.Advances in neural information processing systems, 24, 2011

Reference 4

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Dota 2 with Large Scale Deep Reinforcement Learning

Reference 5

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Observation 69e4e99d-db93-44ca-87ab-73a3456018e7 · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Leveragingproceduralgeneration to benchmark reinforcement learning

Reference 6

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Observation bf416b4e-2a73-4618-b2a4-6cb785d7c7a8 · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Quantifyinggeneraliza- tion in reinforcement learning

Reference 7

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Observation df78588f-6206-4996-84be-181575057466 · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 8

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Observation 4b1e7abb-4055-446f-91e2-ee33fb0bb19d · outbound

This paper cites Degrave, F.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Degrave, F

Reference 9

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Observation 4f25d8e3-dd32-4e81-a310-cf8d1c7cb824 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 1c6db22e-1bfd-479a-916b-4371b0228b12 · outbound

This paper cites Dulac-Arnold, N.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Dulac-Arnold, N

Reference 11

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Observation 2585bacb-d440-4f3a-a92b-fbd2b039682d · outbound

This paper cites Ghosh, J.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Ghosh, J

Reference 12

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Observation f6b14c88-ba9b-497a-a7c6-182b06b9c4d0 · outbound

This paper cites Deep Recurrent Q-Learning for Partially Observable MDPs.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Deep Recurrent Q-Learning for Partially Observable MDPs

Reference 13

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Observation ef9c6021-d62e-43b7-942b-174edf5bb840 · outbound

This paper cites Hochreiter and J.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Hochreiter and J

Reference 14

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Observation 5215ac3d-f50d-454a-8268-db30920c14a4 · outbound

This paper cites Huang, R.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Huang, R

Reference 15

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Hutter, H

Reference 16

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 17

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This paper cites Cybersecurity workforce study.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Cybersecurity workforce study

Reference 18

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This paper cites NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

Reference 19

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Observation af6533b2-f4dc-4823-bb43-c3d5942094af · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

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Observation c2063417-f2a5-43e9-976b-287b76b93851 · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 21

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Observation c686a1b8-9e5f-4370-9642-4bb3c3c3f058 · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 22

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 24

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Observation b934f38d-5405-43ec-8b8f-d95724659b2f · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 25

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 26

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Observation d45b250d-d372-4043-bc0d-94d3ef81e9ae · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Macaulay

Reference 27

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 28

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Technical guide to information security testing and assessment

Reference 29

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Assessing security and privacy controls in information systems and organizations

Reference 30

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Observation e2e76536-4b41-48e8-9b76-5adb3d8f272b · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 31

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Oesch, A

Reference 32

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Parisotto, F

Reference 33

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Patterson, S

Reference 34

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Pleines, M

Reference 35

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 36

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 37

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Raffin, A

Reference 38

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Observation 86bd93f7-8500-41e5-8d32-b857f82b498e · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Automatedpenetrationtestingbasedonlstm and advanced curiosity exploration

Reference 39

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Observation 529d35b1-a25a-4617-b55e-90b5a857de53 · outbound

This paper cites Penetration Testing == POMDP Solving?.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Penetration Testing == POMDP Solving?

Reference 40

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Observation fad18a99-ec48-4443-8b72-943fa1d8ac6b · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Schulman, S

Reference 41

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Observation dd98381a-f26d-48c7-a3fa-3a1cfdc6f22e · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Proximal Policy Optimization Algorithms

Reference 42

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Schwartz and H

Reference 43

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Silver, J

Reference 44

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation CybORG: A Gym for the Development of Autonomous Cyber Agents

Reference 45

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 46

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 47

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 48

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Observation abe9022e-9c4f-40b7-8417-a9dd012a201a · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Terranova, A

Reference 49

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Observation 7c050729-9864-4b22-a692-59ada23c098e · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 50

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Observation 7bb3ba7d-f30e-4797-9a28-dba28c9705b4 · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Vaswani, N

Reference 51

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 52

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Global cybersecurity outlook 2023.Insight Report, 2023

Reference 53

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Yang and X

Reference 54

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation A Study on Overfitting in Deep Reinforcement Learning

Reference 55

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Observation 284a1faa-5b62-4786-8ad5-0b3198941490 · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Zhang, J

Reference 56

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Observation 77f777e9-2310-46b4-93b4-2084c75bc8be · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Unresolved cited work

Reference 57

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation 23 Table 2: Hyperparameter search ranges for PPO, PPO-FS and PPO-AO

Reference 2021

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Observation f9f3d008-a026-4ce2-8099-b3a8898ef15b · outbound

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach

Reference 2022

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation The specific version we used for thestable-baselines3 framework is 2.4

Reference 7602

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Pith citing papers

Observation fcd9be66-14d4-4393-9be7-407e6fbfb17b · inbound

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity cites this paper.

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation

Reference 120

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