From: Adversarial attack and defense in reinforcement learning-from AI security view
Method | Black/White box | Parameters | Target algorithm | Learning | Examples generated | Application scenario | Attack effect |
---|---|---|---|---|---|---|---|
FGSM (Goodfellow et al. 2014a) | White Box | θ,x,y | None | One shot | True | Atari Game | Taking wrong action |
SPA (Xiang et al. 2018) | White Box | VQ,Pstart | Q-Learning | One shot | True | Path Planning | No normal path planning |
WBA (Bai et al. 2018) | White Box | VQ,x | DQN | One shot | True | Path Planning | No normal path planning |
CDG (Chen et al. 2018b) | White Box | V,x | A3C | One shot | True | Path Planning | Unable to reach destination/Time increased |
PIA (Behzadan and Munir 2017) | Black Box | None | DQN | Iterative | True | Atari Game | Taking wrong action |
STA (Lin et al. 2017) | Black Box | None | None | Iterative | True | Atari Game | Taking wrong action |
EA (Lin et al. 2017) | Black Box | None | None | Iterative | True | Atari Game | Taking wrong action |
AVI (Liu et al. 2017) | Black Box | None | VIN | One shot | True | Path Planning | No normal path planning |