Adversarial attacks against conventional Deep Learning (DL) systems and
algorithms have been widely studied, and various defenses were proposed.
However, the possibility and feasibility of such attacks against Deep
Reinforcement Learning (DRL) are less explored. As DRL has achieved great
success in various complex tasks, designing effective adversarial attacks is an
indispensable prerequisite towards building robust DRL algorithms. In this
paper, we introduce two novel adversarial attack techniques to
\emph{stealthily} and \emph{efficiently} attack the DRL agents. These two
techniques enable an adversary to inject adversarial samples in a minimal set
of critical moments while causing the most severe damage to the agent. The
first technique is the \emph{critical point attack}: the adversary builds a
model to predict the future environmental states and agent's actions, assesses
the damage of each possible attack strategy, and selects the optimal one. The
second technique is the \emph{antagonist attack}: the adversary automatically
learns a domain-agnostic model to discover the critical moments of attacking
the agent in an episode. Experimental results demonstrate the effectiveness of
our techniques. Specifically, to successfully attack the DRL agent, our
critical point technique only requires 1 (TORCS) or 2 (Atari Pong and Breakout)
steps, and the antagonist technique needs fewer than 5 steps (4 Mujoco tasks),
which are significant improvements over state-of-the-art methods.

探索针对深度强化学习的对抗攻击，并提出两种攻击技术：关键点攻击和拮抗攻击，以提高鲁棒性，实验结果显示这两种技术比现有方法更具优势。