Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy-Reference-Cited by-同舟云学术

Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy

Published:2023-01-01 Issue:1 Volume:20 Page:758-768
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Mo Kanghua¹^ORCID,Tang Weixuan¹^ORCID,Li Jin¹^ORCID,Yuan Xu²^ORCID

Affiliation:

1. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, Guangdong, China

2. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA, USA

Funder

National Natural Science Foundation of China

Innovation Research for the Postgraduates of Guangzhou University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8858/10016903/09684689.pdf?arnumber=9684689

Reference55 articles.

1. An efficient adversarial example generation algorithm based on an accelerated gradient iterative fast gradient;liu;Comput Standards Interfaces,2021

2. Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain

3. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples;papernot,2016

4. The Limitations of Deep Learning in Adversarial Settings

5. Towards Evaluating the Robustness of Neural Networks

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