  Reinforcement Learning (RL) algorithms have led to recent successes in
solving complex games, such as Atari or Starcraft, and to a huge impact in
real-world applications, such as cybersecurity or autonomous driving. In the
side of the drawbacks, recent works have shown how the performance of RL
algorithms decreases under the influence of soft changes in the reward
function. However, little work has been done about how sensitive these
disturbances are depending on the aggressiveness of the attack and the learning
exploration strategy. In this paper, we propose to fill this gap in the
literature analyzing the effects of different attack strategies based on reward
perturbations, and studying the effect in the learner depending on its
exploration strategy. In order to explain all the behaviors, we choose a
sub-class of MDPs: episodic, stochastic goal-only-rewards MDPs, and in
particular, an intelligible grid domain as a benchmark. In this domain, we
demonstrate that smoothly crafting adversarial rewards are able to mislead the
learner, and that using low exploration probability values, the policy learned
is more robust to corrupt rewards. Finally, in the proposed learning scenario,
a counterintuitive result arises: attacking at each learning episode is the
lowest cost attack strategy.
