Paper Citation Record · LEDGER

Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1703.06748.

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

pith.paper-citation-record.v1
1703.06748 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:37:05.207213Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T21:36:34.386608Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 15c1dd64-3394-49da-b50c-dcc9b038d3f5 · inbound

Scaling Laws for Reward Model Overoptimization cites this paper.

Scaling Laws for Reward Model Overoptimization Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:04:53.322778Z

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.

Observation babecf3e-e3f9-4818-8682-b7b87f483782 · 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 Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T04:37:05.207213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 811dab74-6217-44e7-b000-3c1fa73513d0 · inbound

How Adversarial Environments Mislead Agentic AI? cites this paper.

How Adversarial Environments Mislead Agentic AI? Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:11:07.853050Z

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.

Observation 3cdddeaa-d6a3-42c0-a879-daca9cd0db6d · inbound

TRAP: Tail-aware Ranking Attack for World-Model Planning cites this paper.

TRAP: Tail-aware Ranking Attack for World-Model Planning Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:56:04.872020Z

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.

Observation b8cb2d48-1dec-4e80-814a-a92dd897ca8b · inbound

Efficient Preference Poisoning Attack on Offline RLHF cites this paper.

Efficient Preference Poisoning Attack on Offline RLHF Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 126

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:50:27.186769Z

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.

Observation 06bec4e4-c984-44dc-bd35-55fd927d4684 · inbound

Safe-RULE: Safe Reinforcement UnLEarning cites this paper.

Safe-RULE: Safe Reinforcement UnLEarning Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:07:28.377792Z

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.

Observation 04d5a31c-48c6-433a-b212-7840db8cafab · inbound

RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning cites this paper.

RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:04:21.117818Z

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.

Observation 0aef7773-2766-4344-a304-8def45c8ee33 · inbound

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies cites this paper.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.387878Z

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.