Adversarial Policies: Attacking Deep Reinforcement Learning

ICLR 2020 Adam GleaveMichael DennisCody WildNeel KantSergey LevineStuart Russell

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations... (read more)

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