Paper Citation Record · LEDGER

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.08961.

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pith.paper-citation-record.v1
2506.08961 v1

Coverage vector

measured 41 of 41 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation d86470bc-fb3e-4885-a094-9d41c24b40ec · outbound

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

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Robust deep reinforcement learning against adversarial perturbations on state observations,

Reference 1

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

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Robust deep reinforcement learning through adversarial loss,

Reference 2

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This paper cites Robust reinforcement learning on state observations with learned optimal adversary,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Robust reinforcement learning on state observations with learned optimal adversary,

Reference 3

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This paper cites Efficient adversarial training without attacking: Worst-case-aware robust reinforcement learning,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Efficient adversarial training without attacking: Worst-case-aware robust reinforcement learning,

Reference 4

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This paper cites Spa- tiotemporally constrained action space attacks on deep reinforcement learning agents,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Spa- tiotemporally constrained action space attacks on deep reinforcement learning agents,

Reference 5

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This paper cites Ad- versarial poisoning attacks on reinforcement learning-driven energy pricing,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Ad- versarial poisoning attacks on reinforcement learning-driven energy pricing,

Reference 6

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This paper cites Trojdrl: Trojan attacks on deep reinforcement learning agents. in proc. 57th acm/ieee design automation conference (dac), 2020, march 2020,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Trojdrl: Trojan attacks on deep reinforcement learning agents. in proc. 57th acm/ieee design automation conference (dac), 2020, march 2020,

Reference 7

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This paper cites Adversarial policies: Attacking deep reinforcement learning,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Adversarial policies: Attacking deep reinforcement learning,

Reference 8

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This paper cites On the robustness of cooperative multi-agent reinforcement learning,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation On the robustness of cooperative multi-agent reinforcement learning,

Reference 9

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This paper cites Towards comprehensive testing on the robustness of cooperative multi-agent reinforcement learning,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Towards comprehensive testing on the robustness of cooperative multi-agent reinforcement learning,

Reference 10

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Observation b9e84063-cb85-4c49-af03-05d45a2b9312 · outbound

This paper cites On the utility of learning about humans for human-ai coordination,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation On the utility of learning about humans for human-ai coordination,

Reference 11

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Collaborating with humans without human data,

Reference 12

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Coordination with humans via strategy matching,

Reference 13

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This paper cites Learning zero-shot cooperation with humans, assuming humans are biased,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Learning zero-shot cooperation with humans, assuming humans are biased,

Reference 14

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This paper cites An efficient end-to-end training approach for zero-shot human-ai coordination,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation An efficient end-to-end training approach for zero-shot human-ai coordination,

Reference 15

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This paper cites Intriguing properties of neural networks,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Intriguing properties of neural networks,

Reference 16

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This paper cites One pixel attack for fooling deep neural networks,.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation One pixel attack for fooling deep neural networks,

Reference 17

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This paper cites Adversarial Attacks on Neural Network Policies.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Adversarial Attacks on Neural Network Policies

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

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Vulnerability of deep reinforcement learning to policy induction attacks,

Reference 19

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Tactics of adversarial attack on deep reinforcement learning agents,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Toward evaluating robustness of deep reinforce- ment learning with continuous control,

Reference 21

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Real-time adversarial perturbations against deep reinforcement learning policies: attacks and defenses,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Targeted attack on deep rl-based autonomous driving with learned visual patterns,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Improving robustness of deep reinforcement learning agents: Environment attack based on the critic network,

Reference 24

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Characterizing attacks on deep reinforcement learning,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Rigorous agent evaluation: An adversarial approach to uncover catastrophic failures,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Scalable initial state interdiction for factored mdps,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Robust adversar- ial reinforcement learning,

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

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Robust deep reinforcement learning with adversarial attacks,

Reference 29

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Robust deep reinforcement learning through bootstrapped opportunistic curriculum,

Reference 30

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This paper cites Online Robustness Training for Deep Reinforcement Learning.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Online Robustness Training for Deep Reinforcement Learning

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Certified adversarial robustness for deep reinforcement learning,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Certifiable robustness to adversarial state uncertainty in deep reinforcement learning,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Goal misgeneralization in deep reinforcement learning,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Distilling the Knowledge in a Neural Network

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Distillation as a defense to adversarial perturbations against deep neural networks,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Mujoco: A physics engine for model-based control,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Leveraging procedu- ral generation to benchmark reinforcement learning,

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Proximal Policy Optimization Algorithms

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Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration

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This paper cites A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning.

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

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