Reinforcement learning research experienced substantial jumps in its progress after the first achievement on utilizing deep neural networks to approximate the state-action value function in high-dimensional states. While deep reinforcement learning algorithms are currently being employed in many different tasks from industrial control to biomedical applications, the fact that an MDP has to provide a clear reward function limits the tasks that can be achieved via reinforcement learning. In this line of research, some studies proposed to directly learn a policy from observing expert trajectories (i.e. imitation learning), and others proposed to learn a reward function from the expert demonstrations (i.e. inverse reinforcement learning). In this paper we will focus on robustness and vulnerabilities of deep imitation learning and deep inverse reinforcement learning policies. Furthermore, we will layout non-robust features learnt by the deep inverse reinforcement learning policies. We conduct experiments in the Arcade Learning Environment (ALE), and compare the non-robust features learnt by the deep inverse reinforcement learning algorithms to vanilla trained deep reinforcement learning policies. We hope that our study can provide a basis for the future discussions on the robustness of both deep inverse reinforcement learning and deep reinforcement learning.