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Defending Observation Attacks in Deep Reinforcement Learning via
  Detection and Denoising

Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising

14 June 2022
Zikang Xiong
Joe Eappen
He Zhu
Suresh Jagannathan
    AAML
ArXivPDFHTML

Papers citing "Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising"

3 / 3 papers shown
Title
Seeing is not Believing: Robust Reinforcement Learning against Spurious
  Correlation
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Wenhao Ding
Laixi Shi
Yuejie Chi
Ding Zhao
OOD
27
18
0
15 Jul 2023
Recover Triggered States: Protect Model Against Backdoor Attack in
  Reinforcement Learning
Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning
Hao Chen
Chen Gong
Yizhen Wang
Xinwen Hou
AAML
24
1
0
01 Apr 2023
Robust Reinforcement Learning on State Observations with Learned Optimal
  Adversary
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
Huan Zhang
Hongge Chen
Duane S. Boning
Cho-Jui Hsieh
67
162
0
21 Jan 2021
1