Non-Robust Feature Mapping in Deep Reinforcement Learning

Adversarial perturbations to state observations can dramatically degrade the performance of deep reinforcement learning policies, and thus raise concerns regarding the robustness of deep reinforcement learning agents. A sizeable body of work has focused on addressing the robustness problem in deep reinforcement learning, and there are several recent proposals for adversarial training methods in the deep reinforcement learning domain. In our work we focus on the robustness of state-of-the-art adversarially trained deep reinforcement learning policies and vanilla trained deep reinforcement learning polices. We propose two novel algorithms to map non-robust features in deep reinforcement learning policies. We conduct several experiments in the Arcade Learning Environment (ALE), and with our proposed feature mapping algorithms we show that while the state-of-the-art adversarial training method eliminates a certain set of non-robust features, a new set of non-robust features more intrinsic to the adversarial training are created. Our results lay out concerns that arise when using existing state-of-the-art adversarial training methods, and we believe our proposed feature mapping algorithm can aid in the process of building more robust deep reinforcement learning policies.