Paper Citation Record · LEDGER

Adversarial Policies: Attacking Deep Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:1905.10615.

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

pith.paper-citation-record.v1
1905.10615 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:37:33.643023Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T12:55:44.528298Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 38be4d4c-4d94-425a-b9ca-ae9d9270865c · inbound

A Minimax Approach to Ad Hoc Teamwork cites this paper.

A Minimax Approach to Ad Hoc Teamwork Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T12:34:10.546045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb971443-cc8b-4b56-b18e-eaf7df933a32 · inbound

How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies cites this paper.

How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:32:28.041897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6a3ba601-d6c1-4ce7-aef7-cc5dab0bda08 · inbound

RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation cites this paper.

RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:31:58.552488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a3643ad-4f4c-4b8e-8535-4ef9a2443076 · inbound

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation cites this paper.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T04:37:05.320640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07502c7d-f9f7-4b38-8f47-52cc623fbbaa · inbound

Virtual Agent Economies cites this paper.

Virtual Agent Economies Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T18:09:37.034805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dd19fe0-001c-4349-878b-1f6fa5494205 · inbound

Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning cites this paper.

Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:51:33.866773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 15c259b0-7f1e-4ef1-94e3-72f758120ec0 · inbound

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

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T03:15:09.929179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6789ed48-93f4-43ea-a882-683c59d278d5 · inbound

Discovering Failure Modes in Vision-Language Models using RL cites this paper.

Discovering Failure Modes in Vision-Language Models using RL Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:05:48.683079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1ba2accc-ce3a-404c-b549-d6e54d7edd8c · inbound

Robust Adversarial Policy Optimization Under Dynamics Uncertainty cites this paper.

Robust Adversarial Policy Optimization Under Dynamics Uncertainty Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:26:03.547395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c1f4a11e-6f78-4cac-a37f-f59b89c6d772 · inbound

Efficient Preference Poisoning Attack on Offline RLHF cites this paper.

Efficient Preference Poisoning Attack on Offline RLHF Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 130

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:50:27.142672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b7d7088-9e33-45fe-95f4-a83678eeb201 · inbound

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning cites this paper.

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:26:38.693020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 56867355-8a02-4eea-830a-e9fb5cb74b2c · inbound

Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations cites this paper.

Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-01T12:55:44.530115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3effdda8-2776-49c7-b4eb-8aaea1541e05 · inbound

Robust Critics: Defending LLMs Against Multi-Turn Attacks cites this paper.

Robust Critics: Defending LLMs Against Multi-Turn Attacks Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T13:17:50.484506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f7258b4-f119-478e-9c91-1dc5ae02f1ac · inbound

Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks cites this paper.

Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks Adversarial Policies: Attacking Deep Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T14:37:33.643023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.