Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples, namely the strategically-timed attack and the enchanting attack. In the 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... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
Softmax
Output Functions
A3C
Policy Gradient Methods
Q-Learning
Off-Policy TD Control
Dense Connections
Feedforward Networks
Convolution
Convolutions
DQN
Q-Learning Networks