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Papers
2011.07114
Cited By

Query-based Targeted Action-Space Adversarial Policies on Deep
Reinforcement Learning Agents

v1v2 (latest)

Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents

International Conference on Cyber-Physical Systems (ICCPS), 2020

13 November 2020

Yasaman Esfandiari

ArXiv (abs)PDF HTML

Papers citing "Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents"

13 / 13 papers shown

Provably Efficient Action-Manipulation Attack Against Continuous
Reinforcement Learning

Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning

258

1

0

20 Nov 2024

Beyond Worst-case Attacks: Robust RL with Adaptive Defense via
Non-dominated Policies

Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies

Furong Huang

347

10

0

20 Feb 2024

Optimal Attack and Defense for Reinforcement Learning

Optimal Attack and Defense for Reinforcement LearningAAAI Conference on Artificial Intelligence (AAAI), 2023

318

18

0

30 Nov 2023

ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure
Events

ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure EventsIEEE Open Journal of Intelligent Transportation Systems (JOITS), 2023

183

2

0

28 Aug 2023

SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent
Reinforcement Learning

SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

Maxwell Standen

237

7

0

11 Jan 2023

Security of Deep Reinforcement Learning for Autonomous Driving: A Survey

Security of Deep Reinforcement Learning for Autonomous Driving: A Survey

Christian Scano

Battista Biggio

378

4

0

12 Dec 2022

Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via
Model Checking

Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model CheckingInternational Conference on Agents and Artificial Intelligence (ICAART), 2022

214

4

0

10 Dec 2022

Efficient Adversarial Training without Attacking: Worst-Case-Aware
Robust Reinforcement Learning

Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningNeural Information Processing Systems (NeurIPS), 2022

Furong Huang

256

64

0

12 Oct 2022

A Search-Based Testing Approach for Deep Reinforcement Learning Agents

A Search-Based Testing Approach for Deep Reinforcement Learning AgentsIEEE Transactions on Software Engineering (TSE), 2022

Amirhossein Zolfagharian

Manel Abdellatif

Lionel C. Briand

461

38

0

15 Jun 2022

Adversarial Deep Reinforcement Learning for Improving the Robustness of
Multi-agent Autonomous Driving Policies

Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesAsia-Pacific Software Engineering Conference (APSEC), 2021

291

25

0

22 Dec 2021

A Graph Policy Network Approach for Volt-Var Control in Power
Distribution Systems

A Graph Policy Network Approach for Volt-Var Control in Power Distribution SystemsApplied Energy (Appl. Energy), 2021

153

40

0

24 Sep 2021

Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion
Attacks in Deep RL

Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RLInternational Conference on Learning Representations (ICLR), 2021

Furong Huang

390

82

0

09 Jun 2021

Challenges and Countermeasures for Adversarial Attacks on Deep
Reinforcement Learning

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement LearningIEEE Transactions on Artificial Intelligence (IEEE TAI), 2020

Ala I. Al-Fuqaha

Dusit Niyato

350

185

0

27 Jan 2020

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