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2. Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
 
 # Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

  ![](/sites/default/files/publications/MsPacman.gif) 

 We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples: Strategically-timed attack: the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode. Limiting the attack activity to this subset helps prevent detection of the attack by the agent. We propose a novel method to determine when an adversarial example should be crafted and applied. Enchanting attack: the adversary aims at luring the agent to a designated target state. This is achieved by combining a generative model and a planning algorithm: while the generative model predicts the future states, the planning algorithm generates a preferred sequence of actions for luring the agent. A sequence of adversarial examples are then crafted to lure the agent to take the preferred sequence of actions. We apply the two tactics to the agents trained by the state-of-the-art deep reinforcement learning algorithm including DQN and A3C. In 5 Atari games, our strategically-timed attack reduces as much reward as the uniform attack (i.e., attacking at every time step) does by attacking merely 25% of timesteps on average. Our enchanting attack lures the agent toward designated target states with a more than 70% success rate.



 ## Authors



Yen-Chen Lin (NTHU, TAIWAN)

Zhang-Wei Hong (NTHU, TAIWAN)

Yuan-Hong Liao (NTHU, TAIWAN)

Meng-Li Shi (NTHU, TAIWAN)

[Ming-Yu Liu](/person/ming-yu-liu)

Min Sun (NTHU, TAIWAN)

 

 

 ## Publication Date



Saturday, August 19, 2017

 

 ## Published in



[IJCAI](https://ijcai-17.org/)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

 

 

 ## External Links



[Project website](http://yclin.me/adversarial_attack_RL)