Abstract: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 both DQN and A3C agents are vulnerable to our proposed tactics of adversarial attack.
TL;DR:We propose two tactics of adversarial attacks for deep reinforcement learning and show their strength.
Keywords:Deep learning, Reinforcement Learning
Conflicts:nvidia.com, nthu.edu.tw, merl.com
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