Paper Citation Record · LEDGER

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 2 inbound Pith citation observations for arXiv:2507.17070.

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

pith.paper-citation-record.v1
2507.17070 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:00:57.690797Z

measured 43 of 43 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T07:01:04.860047Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T07:04:21.119128Z

Reference resolution

41 of 41 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2d1d629-9231-4afc-a5f0-b3bd1f62ce85 · outbound

This paper cites Challenges and countermeasures for adversarial at- tacks on deep reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Challenges and countermeasures for adversarial at- tacks on deep reinforcement learning,

Reference 1

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Observation 9890083e-d429-45c6-98a5-80fa31da6448 · outbound

This paper cites A survey on adversarial attacks and defenses in reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach A survey on adversarial attacks and defenses in reinforcement learning,

Reference 2

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Observation 2e79d2fb-9f68-470e-a279-cac019aa79e8 · outbound

This paper cites Anti-plane surface waves in media with surface structure: discrete vs. continuum model.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Anti-plane surface waves in media with surface structure: discrete vs. continuum model

Reference 3

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This paper cites A review of applications of artificial intelligence and blockchain in the energy sector,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach A review of applications of artificial intelligence and blockchain in the energy sector,

Reference 4

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This paper cites Adversarial attacks on neural network policies,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Adversarial attacks on neural network policies,

Reference 5

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Observation a12c2e59-a6e1-4832-8e07-90fb0933bd3f · outbound

This paper cites Comment on "Relation between scattering amplitude and Bethe-Salpeter wave function in quantum field theory".

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Comment on "Relation between scattering amplitude and Bethe-Salpeter wave function in quantum field theory"

Reference 6

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Observation 57624b68-d8d3-4b20-9866-463b4e7a55dd · outbound

This paper cites Robust physical-world attacks on deep learning visual classification,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Robust physical-world attacks on deep learning visual classification,

Reference 7

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Observation 817b9a12-25c6-4cff-ab90-a41ebe10dcb5 · outbound

This paper cites Explaining and harness- ing adversarial examples,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Explaining and harness- ing adversarial examples,

Reference 8

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Observation 5acd409e-4bdc-4b0b-b985-130b8cd1d536 · outbound

This paper cites Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks

Reference 9

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Observation 60061ba1-a4f1-4138-bc84-9963fc066b24 · outbound

This paper cites An environment for autonomous driving decision- making,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach An environment for autonomous driving decision- making,

Reference 10

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Observation 910061f0-f936-47c1-8992-b1d7f488f09e · outbound

This paper cites Perceiver hopfield pooling for dynamic multi-modal and multi-instance fusion,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Perceiver hopfield pooling for dynamic multi-modal and multi-instance fusion,

Reference 11

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Observation 602207ce-bf46-47c9-86a9-0eaf7f50d8d2 · outbound

This paper cites A survey on attacks and their coun- termeasures in deep learning: Applications in deep neural networks, federated, transfer, and deep reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach A survey on attacks and their coun- termeasures in deep learning: Applications in deep neural networks, federated, transfer, and deep reinforcement learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:00:58.212428Z

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Observation ca52872b-9a9d-4f9d-93bc-572a5c88d443 · outbound

This paper cites Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding

Reference 13

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Observation 173f3584-1698-4fb2-b3a3-523ad9e8a260 · outbound

This paper cites Adversarial attacks on neural network policies,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Adversarial attacks on neural network policies,

Reference 14

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Source-reported events for the cited work

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Observation 15136c24-74eb-464d-aeac-02f4a514bbda · outbound

This paper cites Vulnerability of deep reinforcement learning to policy induction attacks,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Vulnerability of deep reinforcement learning to policy induction attacks,

Reference 15

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Observation 5522851d-1202-496b-bd1d-9cdf2066b8dc · outbound

This paper cites Delving into adversarial attacks on deep policies,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Delving into adversarial attacks on deep policies,

Reference 16

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Source-reported events for the cited work

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Observation 1e930cb6-4b46-479e-8d20-f127bf7479af · outbound

This paper cites A malicious attack on the machine learning policy of a robotic system,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach A malicious attack on the machine learning policy of a robotic system,

Reference 17

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Source-reported events for the cited work

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Observation 5239040d-365c-4c07-ba71-30638ef70846 · outbound

This paper cites Copycat: Taking control of neural policies with constant attacks,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Copycat: Taking control of neural policies with constant attacks,

Reference 18

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Observation d96eaa9a-5404-4017-a687-e50fefcd5e27 · outbound

This paper cites Acadia: Efficient and robust adversarial attacks against deep reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Acadia: Efficient and robust adversarial attacks against deep reinforcement learning,

Reference 19

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Observation 5dc85bb0-227e-41af-bc32-c7ce7c76b9a2 · outbound

This paper cites Adversarial attacks in consensus-based multi-agent reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Adversarial attacks in consensus-based multi-agent reinforcement learning,

Reference 20

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Observation 95bce60b-a025-4b0a-900b-ca63c9e626ef · outbound

This paper cites Tactics of adversarial attack on deep reinforcement learning agents,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Tactics of adversarial attack on deep reinforcement learning agents,

Reference 21

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Observation dcc9b26d-374c-4817-ba84-1966882f39c9 · outbound

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Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Sequential attacks on agents for long-term adversarial goals,

Reference 22

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This paper cites Trojdrl: Trojan at- tacks on deep reinforcement learning agents,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Trojdrl: Trojan at- tacks on deep reinforcement learning agents,

Reference 23

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Observation a84d6b37-87ab-48a0-b403-ee6d6c50165f · outbound

This paper cites Stealthy and efficient adversarial attacks against deep reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Stealthy and efficient adversarial attacks against deep reinforcement learning,

Reference 24

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Observation 39993b91-3c61-4303-8d7e-09cc20a89a47 · outbound

This paper cites Provably efficient black-box action poisoning attacks against reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Provably efficient black-box action poisoning attacks against reinforcement learning,

Reference 25

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Observation dabeefeb-4202-47bb-9319-b9b2f113cf63 · outbound

This paper cites Strategically-timed state- observation attacks on deep reinforcement learning agents,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Strategically-timed state- observation attacks on deep reinforcement learning agents,

Reference 26

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Observation 63a92385-4d5b-49b7-b94e-7b4b04a3e90b · outbound

This paper cites Robust deep reinforcement learning with adversarial attacks,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Robust deep reinforcement learning with adversarial attacks,

Reference 27

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Observation 50714ca1-1e70-4c51-8bfd-a05828cff298 · outbound

This paper cites Deep reinforcement learning with robust and smooth policy,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Deep reinforcement learning with robust and smooth policy,

Reference 28

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Observation 4e150d26-eeaa-4078-9931-a623a384ae5b · outbound

This paper cites Robust reinforce- ment learning on state observations with learned optimal adversary,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Robust reinforce- ment learning on state observations with learned optimal adversary,

Reference 29

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Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Action robust reinforcement learning and applications in continuous control,

Reference 30

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This paper cites Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight

Reference 31

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This paper cites A pca-based model to predict adversarial examples on q-learning of path finding,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach A pca-based model to predict adversarial examples on q-learning of path finding,

Reference 32

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Observation feb37fde-8bda-4d96-957e-d5466711557b · outbound

This paper cites Optimal Attacks on Reinforcement Learning Policies.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Optimal Attacks on Reinforcement Learning Policies

Reference 33

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This paper cites Robust deep reinforcement learning against adversarial perturbations on state observations,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Robust deep reinforcement learning against adversarial perturbations on state observations,

Reference 34

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Observation 9ccd6462-8518-4031-bcf8-dcc6cad74966 · outbound

This paper cites Online Robustness Training for Deep Reinforcement Learning.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Online Robustness Training for Deep Reinforcement Learning

Reference 35

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Observation 2fd0a059-780f-47f0-a074-61b7f81d85f0 · outbound

This paper cites Rl-vaegan: Adversarial defense for reinforcement learning agents via style transfer,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Rl-vaegan: Adversarial defense for reinforcement learning agents via style transfer,

Reference 36

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Observation e61f5d43-90b2-452f-bbdf-76af83a0994d · outbound

This paper cites Certified adversarial robustness for deep reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Certified adversarial robustness for deep reinforcement learning,

Reference 37

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Observation 647ab421-c57d-4060-8e3c-9735c1d73ff3 · outbound

This paper cites Robust deep reinforcement learning through adversarial loss,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Robust deep reinforcement learning through adversarial loss,

Reference 38

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Observation efd12c68-667c-4ee8-89c5-4b18cada1355 · outbound

This paper cites Adversarial attacks and defense in deep reinforcement learning (DRL)-based traffic signal controllers,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Adversarial attacks and defense in deep reinforcement learning (DRL)-based traffic signal controllers,

Reference 39

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Observation c2cc25dd-5c59-46e3-a36b-5278d7746d60 · outbound

This paper cites The evolution of criticality in deep reinforcement learning,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach The evolution of criticality in deep reinforcement learning,

Reference 40

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This paper cites Stable-Baselines3: Reliable reinforcement learning im- plementations,.

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Stable-Baselines3: Reliable reinforcement learning im- plementations,

Reference 41

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Pith citing papers

Observation a334e2e1-6de8-4c2a-a8c2-34e58f139ffc · inbound

Real-Time Evaluation of Autonomous Systems under Adversarial Attacks cites this paper.

Real-Time Evaluation of Autonomous Systems under Adversarial Attacks Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach

Reference 7

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Observation 79908241-b3c5-4e8d-b574-46dddd3606d4 · 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 Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach

Reference 5

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Source-reported events for the cited work

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