  We study reward poisoning attacks on online deep reinforcement learning
(DRL), where the attacker is oblivious to the learning algorithm used by the
agent and the dynamics of the environment. We demonstrate the intrinsic
vulnerability of state-of-the-art DRL algorithms by designing a general,
black-box reward poisoning framework called adversarial MDP attacks. We
instantiate our framework to construct two new attacks which only corrupt the
rewards for a small fraction of the total training timesteps and make the agent
learn a low-performing policy. We provide a theoretical analysis of the
efficiency of our attack and perform an extensive empirical evaluation. Our
results show that our attacks efficiently poison agents learning in several
popular classical control and MuJoCo environments with a variety of
state-of-the-art DRL algorithms, such as DQN, PPO, SAC, etc.
