attacks-on-drl 0.2.5

Framework for existing attacks on trained StableBaselines3 DRL policies.

attacks-on-drl

Framework for existing attacks on trained StableBaselines3 DRL policies.

Example Usage

from attacks_on_drl.attacker import FGSMAttacker
from attacks_on_drl.runner import AttackRunner
from attacks_on_drl.victim import DQNVictim

# Defined environment (env) and SB3 policy (policy)
# SB3 policy must be wrapped in a Victim class 
victim = DQNVictim(policy)

attacker = FGSMAttacker(victim)
runner = AttackRunner(env, attacker, victim, episode_max_frames=10_000)

runner.run(n_episodes=10)

Implemented Attacks

1. FGSM $\ell_\infty$ Attacker [1]
2. Value Function Attack [2]
3. FGSM Every N Steps Attacker [2]
4. Strategically Timed Attack [3]
5. Critical Point Attack [4]

Adding A Custom Attack

Any attack must implement the BaseAttacker class, implementing the abstract step method. For example, suppose we introduce an attacker which attacks using FGSM every other step:

from attacks_on_drl.attacker.common import BaseAttacker

class EveryOtherStepAttacker(BaseAttacker):
    def __init__(self, victim: BaseVictim) -> None:
        super().__init__(victim=victim)
        wrapped_victim = VictimModuleWrapper(self.victim)
        self._perturbation_method = FGSM(wrapped_victim, eps=eps)
        
        self.attack = True
    
    def step(self, obs: VecEnvObs) -> tuple[VecEnvObs, bool]:
        if self.attack:
            actions = torch.tensor(self.victim.choose_action(obs, deterministic=True))
            obs = self._perturbation_method(torch.from_numpy(obs)).numpy()
       
        self.attack = not self.attack
        return obs, not self.attack

References

[1] Huang, S., Papernot, N., Goodfellow, I., Duan, Y. and Abbeel, P., 2017. Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284.

[2] Kos, J. and Song, D., 2017. Delving into adversarial attacks on deep policies. arXiv preprint arXiv:1705.06452.

[3] Lin, Y.C., Hong, Z.W., Liao, Y.H., Shih, M.L., Liu, M.Y. and Sun, M., 2017. Tactics of adversarial attack on deep reinforcement learning agents. arXiv preprint arXiv:1703.06748.

[4] Sun, J., Zhang, T., Xie, X., Ma, L., Zheng, Y., Chen, K. and Liu, Y., 2020, April. Stealthy and efficient adversarial attacks against deep reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 04, pp. 5883-5891).