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Tactics of adversarial attack on deep reinforcement learning agents
Conference paper

Tactics of adversarial attack on deep reinforcement learning agents

Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu and Min Sun
5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings
2017

Abstract

Education Computer Science Applications Linguistics and Language Language and Linguistics
We introduce two novel tactics for adversarial attack on deep reinforcement learning (RL) agents: strategically-timed and enchanting attack. For strategically-timed attack, our method selectively forces the deep RL agent to take the least likely action. For enchanting attack, our method lures the agent to a target state by staging a sequence of adversarial attacks. We show that DQN and A3C agents are vulnerable to both tactics. Future work on defending is discussed in App. C.

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